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  • 1. Aaboen, Lise
    et al.
    Löfsten, Hans
    Bengtsson, Lars
    Nourishment for the piggy bank: facilitation of external financing in incubators2011In: International Journal of Technology Transfer and Commercialisation, ISSN 1470-6075, E-ISSN 1741-5284, Vol. 10, no 3/4, p. 354-374Article in journal (Refereed)
    Abstract [en]

    In this paper, we argue that incubators facilitate access to external financing for their incubatees. Incubators use a wide range of activities to facilitate the accessing of external financing from public and private sources. We have grouped these into two sets of activities. The general activities aim to develop the conditions for external financing through information, education of incubatees, network-building and lobbying activities. The specific activities aim to assist the individual incubatee in their pursuit of external finance through help in application procedures, establishing need for capital, making contacts with the best public or private investor, etc. Based on the survey data, we have also shown that it is more common for incubatees to attract external capital compared to non-incubator firms. The incubatees seem especially successful in attracting public capital. The incubatees also attract more private external capital, however, the observed frequency of private capital in the incubatees are low.

  • 2. Abarkan, Abdellah
    The study of urban form in Sweden2009In: Urban Morphology, ISSN 1027-4278 , Vol. 13, no 2, p. 121-127Article in journal (Refereed)
    Abstract [en]

    Early research on urban form in Sweden was undertaken before the First World War. After the Second World War research was influenced by the major criticisms levelled at comprehensive urban renewal and suburban mass housing. These criticisms were particularly on the ground that values embodied in the traditional built environment were being ignored. Increased interest in the study of historical urban fabrics was associated with the development of methodologies reliant on the concepts of typology and morphology. These development were dependent on the activities of individual researchers until the very recent development of wider co-ordinating research organizations.

  • 3.
    Abdelraheem, Mohamed Ahmed
    et al.
    SICS Swedish ICT AB, SWE.
    Gehrmann, Christian
    SICS Swedish ICT AB, SWE.
    Lindström, Malin
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Nordahl, Christian
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Executing Boolean queries on an encrypted Bitmap index2016In: CCSW 2016 - Proceedings of the 2016 ACM Cloud Computing Security Workshop, co-located with CCS 2016, Association for Computing Machinery (ACM), 2016, p. 11-22Conference paper (Refereed)
    Abstract [en]

    We propose a simple and efficient searchable symmetric encryption scheme based on a Bitmap index that evaluates Boolean queries. Our scheme provides a practical solution in settings where communications and computations are very constrained as it offers a suitable trade-off between privacy and performance.

  • 4.
    Abghari, Shahrooz
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Data Modeling for Outlier Detection2018Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis explores the data modeling for outlier detection techniques in three different application domains: maritime surveillance, district heating, and online media and sequence datasets. The proposed models are evaluated and validated under different experimental scenarios, taking into account specific characteristics and setups of the different domains.

    Outlier detection has been studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. Outlier detection techniques are often domain-specific. The main challenge in outlier detection relates to modeling the normal behavior in order to identify abnormalities. The choice of model is important, i.e., an incorrect choice of data model can lead to poor results. This requires a good understanding and interpretation of the data, the constraints, and the requirements of the problem domain. Outlier detection is largely an unsupervised problem due to unavailability of labeled data and the fact that labeled data is expensive.

    We have studied and applied a combination of both machine learning and data mining techniques to build data-driven and domain-oriented outlier detection models. We have shown the importance of data preprocessing as well as feature selection in building suitable methods for data modeling. We have taken advantage of both supervised and unsupervised techniques to create hybrid methods. For example, we have proposed a rule-based outlier detection system based on open data for the maritime surveillance domain. Furthermore, we have combined cluster analysis and regression to identify manual changes in the heating systems at the building level. Sequential pattern mining for identifying contextual and collective outliers in online media data have also been exploited. In addition, we have proposed a minimum spanning tree clustering technique for detection of groups of outliers in online media and sequence data. The proposed models have been shown to be capable of explaining the underlying properties of the detected outliers. This can facilitate domain experts in narrowing down the scope of analysis and understanding the reasons of such anomalous behaviors. We have also investigated the reproducibility of the proposed models in similar application domains.

  • 5.
    Abghari, Shahrooz
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lavesson, Niklas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Gustafsson, Jörgen
    Ericsson AB.
    Shaikh, Junaid
    Ericsson AB.
    Outlier Detection for Video Session Data Using Sequential Pattern Mining2018In: ACM SIGKDD Workshop On Outlier Detection De-constructed, 2018Conference paper (Refereed)
    Abstract [en]

    The growth of Internet video and over-the-top transmission techniqueshas enabled online video service providers to deliver highquality video content to viewers. To maintain and improve thequality of experience, video providers need to detect unexpectedissues that can highly affect the viewers’ experience. This requiresanalyzing massive amounts of video session data in order to findunexpected sequences of events. In this paper we combine sequentialpattern mining and clustering to discover such event sequences.The proposed approach applies sequential pattern mining to findfrequent patterns by considering contextual and collective outliers.In order to distinguish between the normal and abnormal behaviorof the system, we initially identify the most frequent patterns. Thena clustering algorithm is applied on the most frequent patterns.The generated clustering model together with Silhouette Index areused for further analysis of less frequent patterns and detectionof potential outliers. Our results show that the proposed approachcan detect outliers at the system level.

  • 6.
    Abghari, Shahrooz
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lavesson, Niklas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Ickin, Selim
    Ericsson, SWE.
    Gustafsson, Jörgen
    Ericsson, SWE.
    A Minimum Spanning Tree Clustering Approach for Outlier Detection in Event Sequences2018In: 2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) / [ed] Wani M.A.,Sayed-Mouchaweh M.,Lughofer E.,Gama J.,Kantardzic M., IEEE, 2018, p. 1123-1130, article id 8614207Conference paper (Refereed)
    Abstract [en]

    Outlier detection has been studied in many domains. Outliers arise due to different reasons such as mechanical issues, fraudulent behavior, and human error. In this paper, we propose an unsupervised approach for outlier detection in a sequence dataset. The proposed approach combines sequential pattern mining, cluster analysis, and a minimum spanning tree algorithm in order to identify clusters of outliers. Initially, the sequential pattern mining is used to extract frequent sequential patterns. Next, the extracted patterns are clustered into groups of similar patterns. Finally, the minimum spanning tree algorithm is used to find groups of outliers. The proposed approach has been evaluated on two different real datasets, i.e., smart meter data and video session data. The obtained results have shown that our approach can be applied to narrow down the space of events to a set of potential outliers and facilitate domain experts in further analysis and identification of system level issues.

  • 7.
    Abghari, Shahrooz
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    García Martín, Eva
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Johansson, Christian
    NODA Intelligent Systems AB, SWE.
    Lavesson, Niklas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Trend analysis to automatically identify heat program changes2017In: Energy Procedia, Elsevier, 2017, Vol. 116, p. 407-415Conference paper (Refereed)
    Abstract [en]

    The aim of this study is to improve the monitoring and controlling of heating systems located at customer buildings through the use of a decision support system. To achieve this, the proposed system applies a two-step classifier to detect manual changes of the temperature of the heating system. We apply data from the Swedish company NODA, active in energy optimization and services for energy efficiency, to train and test the suggested system. The decision support system is evaluated through an experiment and the results are validated by experts at NODA. The results show that the decision support system can detect changes within three days after their occurrence and only by considering daily average measurements.

  • 8. Aceijas, Carmen
    et al.
    Brall, Caroline
    Schröder-Bäck, Peter
    Otok, Robert
    Maeckelberghe, Els
    Stjernberg, Louise
    Blekinge Institute of Technology, School of Health Science.
    Strech, Daniel
    Tulchinsky, Theodore H
    Teaching Ethics in Schools of Public Health in the European Region: Findings from a Screening Survey2012In: Public Health Reviews, ISSN 0301-0422, E-ISSN 2107-6952, Vol. 34, no 1Article in journal (Refereed)
    Abstract [en]

    A survey targeting ASPHER members was launched in 2010/11, being a first initiative in improving ethics education in European Schools of Public Health. An 8-items questionnaire collected information on teaching of ethics in public health. A 52% response rate (43/82) revealed that almost all of the schools (95% out of 40 respondents with valid data) included the teaching of ethics in at least one of its programmes. They also expressed the need of support, (e.g.: a model curriculum (n=25), case studies (n=24)), which indicates further work to be met by the ASPHER Working Group on Ethics and Values in Public Health.

  • 9.
    Acevedo, Carlos
    Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.
    Developing Inclusive Innovation Processes and Co-Evolutionary University-Society Approaches in Bolivia2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This study is part of a worldwide debate on inclusive innovation systems in developing

    countries and particularly on the co-evolutionary processes taking place, seen from the

    perspective of a public university. The increasing literature that discusses how innovation

    systems and development can foster more inclusive and sustainable societies has

    inspired this thesis work. Thus, the main problem handled in the research concerns the

    question how socially sensitive research practices and policies at a public university in

    Bolivia can be stimulated within emerging innovation system dynamics. In that vein,

    empirical knowledge is developed at the Universidad Mayor de San SimoÅLn (UMSS),

    Cochabamba as a contribution to experience-based learning in the field. Analysis are

    nourished by a dialogue with the work of prominent Latin American scholars and

    practitioners around the idea of a developmental university and the democratization

    of knowledge. The reader will be able to recognize a recursive transit between theory

    and practice, where a number of relevant concepts are contextualized and connected

    in order to enable keys of critical interpretation and paths of practices amplification

    for social inclusion purposes established. The study shows how, based on a previous

    experience, new competences and capacities for the Technology Transfer Unit (UTT)

    at UMSS were produced, in this case transforming itself into a University Innovation

    Centre. Main lessons gained in that experience came from two pilot cluster development

    (food and leather sectors) and a multidisciplinary researchers network (UMSS

    Innovation Team) where insights found can improve future collaborative relations between

    university and society for inclusive innovation processes within the Bolivian

    context.

  • 10. Acevedo Peña, Carlos Gonzalo
    Developing Inclusive Innovation Processes and Co-Evolutionary Approaches in Bolivia2015Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The concept of National Innovation Systems (NIS) has been widely adopted in developing countries, particularly in Latin American countries, for the last two decades. The concept is used mainly as an ex-ante framework to organize and increase the dynamics of those institutions linked to science, technology and innovation, for catching-up processes of development. In the particular case of Bolivia, and after several decades of social and economic crisis, the promise of a national innovation system reconciles a framework for collaboration between the university, the government and the socio-productive sectors. Dynamics of collaboration generated within NIS can be a useful tool for the pursuit of inclusive development ambitions.

     

    This thesis is focused on inclusive innovation processes and the generation of co-evolutionary processes between university, government and socio-productive sectors. This is the result of 8 years of participatory action research influenced by Mode 2 knowledge-production and Technoscientific approaches.

     

    The study explores the policy paths the Bolivian government has followed in the last three decades in order to organize science, technology and innovation. It reveals that Bolivia has an emerging national innovation system, where its demand-pulled innovation model presents an inclusive approach. Innovation policy efforts in Bolivia are led by the Vice-Ministry of Science and Technology (VCyT). Moreover, NIS involves relational and collaborative approaches between institutions, which imply structural and organizational challenges, particularly for public universities, as they concentrate most of the research capabilities in the country. These universities are challenged to participate in NIS within contexts of weak demanding sectors. 

     

    This research focuses on the early empirical approaches and transformations at Universidad Mayor de San Simón (UMSS) in Cochabamba. The aim to strengthen internal innovation capabilities of the university and enhance the relevance of research activities in society by supporting socio-economic development in the framework of innovation systems is led by the Technology Transfer Unit (UTT) at UMSS. UTT has become a recognized innovation facilitator unit, inside and outside the university, by proposing pro-active initiatives to support emerging innovation systems. Because of its complexity, the study focuses particularly on cluster development promoted by UTT. Open clusters are based on linking mechanisms between the university research capabilities, the socio-productive actors and government. Cluster development has shown to be a practical mechanism for the university to meet the demanding sector (government and socio-productive actors) and to develop trust-based inclusive innovation processes. The experiences from cluster activities have inspired the development of new research policies at UMSS, with a strong orientation to foster research activities towards an increased focus on socio-economic development. The experiences gained at UMSS are discussed and presented as a “developmental university” approach.

     

    Inclusive innovation processes with co-evolutionary approaches seem to constitute an alternative path supporting achievement of inclusive development ambitions in Bolivia. 

  • 11.
    Acs, Zoltan J.
    et al.
    London School of Economics and Political Science, GBR.
    Braunerhjelm, Pontus
    Swedish Entrepreneurship Forum, SWE.
    Karlsson, Charlie
    Blekinge Institute of Technology, Faculty of Engineering, Department of Industrial Economics.
    Philippe Aghion: recipient of the 2016 Global Award for Entrepreneurship Research2017In: Small Business Economics, ISSN 0921-898X, E-ISSN 1573-0913, Vol. 48, no 1, p. 1-8Article in journal (Refereed)
    Abstract [en]

    Professor Philippe Aghion is the 2016 recipient of the Global Award for Entrepreneurship Research, consisting of 100,000 Euros and a statuette designed by the internationally renowned Swedish sculptor Carl Milles. He is one of the most influential researchers worldwide in economics in the last couple of decades. His research has advanced our understanding of the relationship between firm-level innovation, entry and exit on the one hand, and productivity and growth on the other. Aghion has thus accomplished to bridge theoretical macroeconomic growth models with a more complete and consistent microeconomic setting. He is one of the founding fathers of the pioneering and original contribution referred to as Schumpeterian growth theory. Philippe Aghion has not only contributed with more sophisticated theoretical models, but also provided empirical evidence regarding the importance of entrepreneurial endeavours for societal prosperity, thereby initiating a more nuanced policy discussion concerning the interdependencies between entrepreneurship, competition, wealth and growth.

  • 12.
    Adamov, Alexander
    et al.
    Kharkiv Natl Univ Radio Elect, NioGuard Secur Lab, Kharkov, Kharkiv Oblast, Ukraine..
    Carlsson, Anders
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    A Sandboxing Method to Protect Cloud Cyberspace2015In: PROCEEDINGS OF 2015 IEEE EAST-WEST DESIGN & TEST SYMPOSIUM (EWDTS), IEEE Communications Society, 2015Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of protecting cloud environments against targeted attacks, which have become a popular mean of gaining access to organization's confidential information and resources of cloud providers. Only in 2015 eleven targeted attacks have been discovered by Kaspersky Lab. One of them - Duqu2 - successfully attacked the Lab itself. In this context, security researchers show rising concern about protecting corporate networks and cloud infrastructure used by large organizations against such type of attacks. This article describes a possibility to apply a sandboxing method within a cloud environment to enforce security perimeter of the cloud.

  • 13.
    Adamov, Alexander
    et al.
    Harkivskij Nacionalnij Universitet Radioelectroniki, UKR.
    Carlsson, Anders
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Cloud incident response model2016In: Proceedings of 2016 IEEE East-West Design and Test Symposium, EWDTS 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of incident response in clouds. A conventional incident response model is formulated to be used as a basement for the cloud incident response model. Minimization of incident handling time is considered as a key criterion of the proposed cloud incident response model that can be done at the expense of embedding infrastructure redundancy into the cloud infrastructure represented by Network and Security Controllers and introducing Security Domain for threat analysis and cloud forensics. These architectural changes are discussed and applied within the cloud incident response model. © 2016 IEEE.

  • 14.
    Adamov, Alexander
    et al.
    Kharkiv National University of Radioelectronics, UKR.
    Carlsson, Anders
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    The state of ransomware: Trends and mitigation techniques2017In: Proceedings of 2017 IEEE East-West Design and Test Symposium, EWDTS 2017, Institute of Electrical and Electronics Engineers Inc. , 2017, article id 8110056Conference paper (Refereed)
    Abstract [en]

    This paper contains an analysis of the payload of the popular ransomware for Windows, Android, Linux, and MacOSX platforms. Namely, VaultCrypt (CrypVault), TeslaCrypt, NanoLocker, Trojan-Ransom.Linux.Cryptor, Android Simplelocker, OSX/KeRanger-A, WannaCry, Petya, NotPetya, Cerber, Spora, Serpent ransomware were put under the microscope. A set of characteristics was proposed to be used for the analysis. The purpose of the analysis is generalization of the collected data that describes behavior and design trends of modern ransomware. The objective is to suggest ransomware threat mitigation techniques based on the obtained information. The novelty of the paper is the analysis methodology based on the chosen set of 13 key characteristics that helps to determine similarities and differences thorough the list of ransomware put under analysis. Most of the ransomware samples presented were manually analyzed by the authors eliminating contradictions in descriptions of ransomware behavior published by different malware research laboratories through verification of the payload of the latest versions of ransomware. © 2017 IEEE.

  • 15. Adams, Liz
    et al.
    Börstler, Jürgen
    What It's Like to Participate in an ITiCSE Working Group2011In: ACM SIGCSE Bulletin, Vol. 43, no 1Article in journal (Other academic)
  • 16. Adams, R.
    et al.
    Fincher, S.
    Pears, A.
    Börstler, Jürgen
    Umeå universitet, Institutionen för datavetenskap.
    Bousted, J.
    Dalenius, P.
    Eken, G.
    Heyer, T.
    Jacobsson, A.
    Lindberg, V.
    Molin, B.
    Moström, J.-E.
    Umeå universitet, Institutionen för datavetenskap.
    Wiggberg, M.
    What is the Word for Engineering in Swedish: Swedish Students' Conceptions of their Discipline2007Report (Other academic)
  • 17. Adams, R
    et al.
    Lindberg, Vanja
    What is the word for engineering in Swedish: students conception of their discipline2007Other (Other academic)
    Abstract [en]

    Engineering education in Sweden as in the rest of the world is experiencing a decline in student interest. There are concerns about the ways in which students think about engineering education, why they join an academic programme in engineering, and why they persist in their studies. In this context the aims of the Nationellt ämnesdidaktiskt Centrum för Teknikutbildning i Studenternas Sammanhang project (CeTUSS) is to investigate the student experience and to identify and support a continuing network of interested researchers, as well as in building capacity for disciplinary pedagogic investigation. The Stepping Stones project brings together these interests in a multi-researcher, multi-institutional study that investigates how students and academic staff perceive engineering in Sweden and in Swedish education. The first results of that project are reported here. As this study is situated uniquely in Swedish education, it allows for exploration of a Swedish perspective on conceptions of engineering. The Stepping Stones project was based on a model of research capacity-building previously instantiated in the USA and Australia (Fincher & Tenenberg, 2006).

  • 18.
    Ademovski, S. Erovic
    et al.
    Kristianstad Univ, Sect Hlth & Soc, S-29188 Kristianstad, Sweden..
    Lingstrom, P.
    Univ Gothenburg, Sahlgrenska Acad, Inst Odontol, Dept Cariol, Gothenburg, Sweden..
    Renvert, Stefan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    The effect of different mouth rinse products on intra-oral halitosis2016In: International Journal of Dental Hygiene, ISSN 1601-5029, E-ISSN 1601-5037, Vol. 14, no 2, p. 117-123Article in journal (Refereed)
    Abstract [en]

    Aim: To evaluate the effect of different mouth rinses 12 h after rinsing on genuine intra-oral halitosis. Materials and Methods: Twenty-four adults with halitosis were included in a double-blind, crossover, randomized clinical trial. Halitosis was evaluated 12 h after rinsing with placebo and five mouth rinse products containing zinc acetate and chlorhexidine diacetate; zinc lactate, chlorhexidine and cetylpyridinium chloride; zinc acetate and chlorhexidine diacetate with reduced amounts of mint and menthol; zinc chloride and essential oil; and chlorine dioxide using the organoleptic method and a gas chromatograph. Test periods were separated by 1 week. Results: Hydrogen sulphide (H2S), methyl mercaptan (MM) and the organoleptic scores (OLS) were significantly reduced 12 h following rinsing with all substances compared to placebo (P < 0.05). H2S was more effectively reduced after rinsing with zinc acetate and chlorhexidine diacetate and zinc acetate and chlorhexidine diacetate with reduced amounts of mint and menthol compared to rinsing with zinc chloride and essential oil (P < 0.05), and significantly lower values of MM were obtained after rinsing with zinc acetate and chlorhexidine diacetate compared to zinc lactate, chlorhexidine and cetylpyridinium chloride (P < 0.05). The percentage effectively treated individuals (H2S (<112 ppb), MM (<26 ppb) and OLS score <2) varied from 58% percentage (zinc acetate and chlorhexidine diacetate) to 26% (zinc chloride and essential oil). Conclusion: All treatments resulted in reduction in halitosis 12 h after rinsing compared to placebo. H2S and MM were most effectively reduced by zinc acetate and chlorhexidine diacetate.

  • 19. Ademovski, Seida Erovic
    et al.
    Lingström, Peter
    Winkel, Edwin
    Tangerman, Albert
    Persson, Rutger
    Renvert, Stefan
    Blekinge Institute of Technology, School of Health Science.
    Comparison of different treatment modalities for oral halitosis2012In: Acta Odontologica Scandinavica, ISSN 0001-6357, E-ISSN 1502-3850, Vol. 70, no 3, p. 224-233Article in journal (Refereed)
    Abstract [en]

    Objectives. To assess the effects on intra-oral halitosis by a mouth rinse containing zinc acetate (0.3%) and chlorhexidine diacetate (0.025%) with and without adjunct tongue scraping. Materials and methods. Twenty-one subjects without a diagnosis of periodontitis were randomized in a cross-over clinical trial. Organoleptic scores (OLS) were assessed to define intra-oral halitosis by total volatile sulfur compound (T-VSC) measurements and by gas chromatography. Results. Twenty-one subjects with a mean age of 45.7 years (SD: +/- 13.3, range: 21-66). The OLS were significantly lower following active rinse combined with tongue scraping (p < 0.001) at all time points. Immediately after, at 30 min, and at day 14, the T-VSC values were lower in the active rinse sequence than in the negative rinse sequence (p < 0.001, p < 0.001 and p < 0.05, respectively). At 30 min and at day 14, the hydrogen sulfide (H2S) and methyl mercaptan (MM) values were lower in the active rinse sequence compared to the inactive rinse sequence (p < 0.001). The inactive rinse sequence with tongue scraping reduced T-VSC at 30 min (p < 0.001) but not at 14 days. Similar reductions in T-VSC, H2S and MM were found in the active rinse sequence with or without tongue scraping. Conclusion. The use of a tongue scraper did not provide additional benefits to the active mouth rinse, but reduced OLS and tongue coating index.

  • 20.
    Ademovski, Seida Erovic
    et al.
    Kristianstad Univ, Sch Hlth & Soc, S-29188 Kristianstad, Sweden..
    Martensson, Carina
    Kristianstad Univ, Sch Hlth & Soc, S-29188 Kristianstad, Sweden..
    Persson, G. Rutger
    Kristianstad Univ, Sch Hlth & Soc, S-29188 Kristianstad, Sweden.;Univ Washington, Sch Dent, Dept Periodont, Seattle, WA 98195 USA..
    Renvert, Stefan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    The effect of periodontal therapy on intra-oral halitosis: a case series2016In: Journal of Clinical Periodontology, ISSN 0303-6979, E-ISSN 1600-051X, Vol. 43, no 5, p. 445-452Article in journal (Refereed)
    Abstract [en]

    Aim: The aim of this study was to evaluate the effects of non-surgical periodontal therapy on intra-oral halitosis 3months after therapy. Material and methods: Sixty-eight adults with intra-oral halitosis were included in a case series. Intra-oral halitosis was evaluated at baseline, and at 3months after treatment using the organoleptic scores (OLS), Halimeter (R), and a gas chromatograph. Results: Significant reductions for OLS (p<0.01), total sum of volatile sulphur compounds (T-VSC) (p<0.01) and methyl mercaptan (MM) (p<0.05) values were found after treatment. Hydrogen sulphide (H2S) levels were not significantly reduced. The numbers of probing pockets 4mm, 5mm and 6mm were significantly reduced as a result of therapy (p<0.001). Bleeding on probing (BOP) and plaque indices were also significantly reduced (p<0.001). For the 34 individuals with successful periodontal treatment (BOP<20% and a 50% reduction of total pocket depth) reductions in OLS (p<0.01) and T-VSC scores (p<0.01) were found. Eleven individuals were considered effectively treated for intra-oral halitosis presenting with a T-VSC value <160ppb, a H2S value <112ppb and a MM value <26ppb. Conclusion: Non-surgical periodontal therapy resulted in reduction of OLS, MM and T-VSC values 3months after therapy. Few individuals were considered as effectively treated for intra-oral halitosis.

  • 21. Ademovski, Seida
    et al.
    Persson, Gösta Rutger
    Winkel, Edwin
    Tangerman, Albert
    Lingström, Peter
    Renvert, Stefan
    Blekinge Institute of Technology, School of Health Science.
    The short-term treatment effects on the microbiota at the dorsum of the tongue in intra-oral halitosis patients-a randomized clinical trial2013In: Clinical Oral Investigations, ISSN 1432-6981, E-ISSN 1436-3771, Vol. 17, no 2, p. 463-473Article in journal (Refereed)
    Abstract [en]

    This study aims to assess the effects of rinsing with zinc- and chlorhexidine-containing mouth rinse with or without adjunct tongue scraping on volatile sulfur compounds (VSCs) in breath air, and the microbiota at the dorsum of the tongue. Material and methods: A randomized single-masked controlled clinical trial with a cross-over study design over 14 days including 21 subjects was performed. Bacterial samples from the dorsum of the tongue were assayed by checkerboard DNA-DNA hybridization. Results: No halitosis (identified by VSC assessments) at day 14 was identified in 12/21 subjects with active rinse alone, in 10/21with adjunct use of tongue scraper, in 1/21 for negative control rinse alone, and in 3/21 in the control and tongue scraping sequence. At day 14, significantly lower counts were identified only in the active rinse sequence (p < 0.001) for 15/78 species including, Fusobacterium sp., Porphyromonas gingivalis, Pseudomonas aeruginosa, Staphylococcus aureus, and Tannerella forsythia. A decrease in bacteria from baseline to day 14 was found in successfully treated subjects for 9/74 species including: P. gingivalis, Prevotella melaninogenica, S. aureus, and Treponema denticola. Baseline VSC scores were correlated with several bacterial species. The use of a tongue scraper combined with active rinse did not change the levels of VSC compared to rinsing alone. Conclusions: VSC scores were not associated with bacterial counts in samples taken from the dorsum of the tongue. The active rinse alone containing zinc and chlorhexidine had effects on intra-oral halitosis and reduced bacterial counts of species associated with malodor. Tongue scraping provided no beneficial effects on the microbiota studied. Clinical relevance: Periodontally healthy subjects with intra-oral halitosis benefit from daily rinsing with zinc- and chlorhexidine-containing mouth rinse.

  • 22. Adolfsson, Stefan
    On Automatic Detection of Burn-through Using a Parametric Model1995Report (Other academic)
  • 23. Adolfsson, Stefan
    Quality Monitoring in Pulsed GMA Welding Using Modern Signal Processing Methods1995Licentiate thesis, comprehensive summary (Other academic)
  • 24. Adolfsson, Stefan
    Quility Monitoring in Robotised Short Circuiting GMA Welding1997Report (Other academic)
    Abstract [en]

    This paper addresses the problem of automatic monitoring the weld quality produced by robotised short arc welding. A simple statistical change detection algorithm for the weld quality, recursive Sequential Probability Ratio Test (SPRT), is used. The algorithm may equivalently be viewed as a cumulative sum (CUSUM) - type test. The test statistics is based upon the variance of the amplitude of the weld voltage. The performance of the algorithm is evaluated using experimental data. The results obtained from the algorithm indicate that it is possible to detect changes in the weld quality automatically and on-line.

  • 25. Adolfsson, Stefan
    et al.
    Bahrami, Ali
    Bolmsjö, Gunnar
    Claesson, Ingvar
    Automatic quality monitoring in robotised GMA welding using a repeated sequential probability ratio test method1997In: International Journal for the Joining of Materials, ISSN 0905-6866, Vol. 9, no 1, p. 2-8Article in journal (Refereed)
  • 26. Adolfsson, Stefan
    et al.
    Bahrami, Ali
    Claesson, Ingvar
    A Sequential Probability Ratio Test Method for Quality Monitoring in Robotised GMA Welding1997Conference paper (Refereed)
    Abstract [en]

    This paper deals with the problem of automatic monitoring the weld quality when welding with Gas Metal Arc (GMA) in short circuiting mode. Experiments with two different types of T-joints are performed in order to provoke optimal and non-optimal welding conditions. During the experiments, voltage and current are measured from the welding process. A simple statistical change detection algorithm for the weld quality, the repeated Sequential Probability Ratio Test (SPRT), is used. The algorithm can equivalently be viewed as a cumulative sum (CUSUM) - type test. The test statistics is based upon the fluctuations of amplitude in the weld voltage. It is shown that the fluctuations of the weld voltage amplitude decreases when the welding process is not operating under optimal condition. The results obtained from the experiments indicate that it is possible to detect changes in the weld quality automatically and on-line.

  • 27. Adolfsson, Stefan
    et al.
    Bahrami, Ali
    Claesson, Ingvar
    Quality Monitoring in Robotised Welding using Sequential Probability Ratio test1996Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of automatic monitoring the weld quality produced by robotised short arc welding. A simple statistical change detection algorithm for the weld quality, recursive sequential probability ratio test (SPRT), is used. The algorithm may equivalently be viewed as a cumulative sum (CUSUM) type test. The test statistics is based upon the variance of the amplitude of the weld voltage. It is shown that the variance of the weld voltage amplitude decreases when the welding process is not operating under optimal condition. The performance of the algorithm is evaluated using experimental data. The results obtained from the algorithm indicate that it is possible to detect changes in the weld quality automatically and on-line

  • 28. Adolfsson, Stefan
    et al.
    Bahrami, Ali
    Claesson, Ingvar
    Bolmsjö, Gunnar
    On-line quality monitoring in short: circuit gas metal arc welding1999In: Welding Journal, ISSN 0043-2296, Vol. 78, no 2, p. 59S-73SArticle in journal (Refereed)
    Abstract [en]

    This paper addresses the problems involved in the automatic monitoring of the weld quality produced by robotized short-arc welding. A simple statistical change detection algorithm for the weld quality, the repeated Sequential Probability Ratio Test (SPRT), was used. The algorithm may similarly be viewed as a cumulative sum (CUSUM) type test, and is well-suited to detecting sudden minor changes in the monitored test statistic. The test statistic is based on the variance of the weld voltage, wherein it will be shown that the variance decreases when the welding process is not operating under optimal conditions. The performance of the algorithm is assessed through the use of experimental data. The results obtained from the algorithm show that it is possible to detect changes in weld quality automatically and on-line.

  • 29. Adolfsson, Stefan
    et al.
    Ericson, Klas
    Grennberg, Anders
    Automatic Detection of Burn-through in GMA Welding Using a Parametric Model1996In: Mechanical Systems & Signal Processing, ISSN 0888-3270 , Vol. 10, no 5, p. 633-651Article in journal (Refereed)
    Abstract [en]

    This paper addresses the problem of automatic detection of burn-through in weld joints. Gas metal are (GMA) welding with pulsed current is used, and welding voltage and current are recorded. As short-circuitings are common between the welding electrode and the work piece during burn-through, a short-circuit detector is developed to detect these events. To detect another specific characteristic of burn-through-a broadband long-lasting voltage component-this detector is combined with a square-law detector. This second detector is based on a non-linear modification of an autoregressive model with extra input (ARX-model) of the welding process. The results obtained from this compound detector indicate that it is possible to detect burn-through in the welds automatically. The work also indicates that it is possible to design an on-line monitoring system for robotic GMA welding.

  • 30. Adolfsson, Stefan
    et al.
    Ericson, Klas
    Gustavsson, Jan-Olof
    Ågren, Björn
    Quality Monitoring for Pulsed Arc Welding1994Conference paper (Refereed)
  • 31. Adolfsson, Stefan
    et al.
    Ericson, Klas
    Ågren, Björn
    On Automatic Detection of Burn-through in GMA Welding: Weld Voltage Analysis1995Report (Other academic)
  • 32. Adolfsson, Vilhelm
    et al.
    Goldberg, Max
    Jawerth, Björna
    Lennerstad, Håkan
    Localized Galerkin Estimates for Boundary Integral Equations on Lipschitz Domanis1992In: SIAM Journal on Mathematical Analysis, Vol. 5, no 23, p. 751-764Article in journal (Refereed)
    Abstract [en]

    The Galerkin method is studied for solving the boundary integral equations associated with the Laplace operator on nonsmooth domains. Convergence is established with a condition on the meshsize, which involves the local curvature on certain approximating domains. Error estimates are also proved, and the results are generalized to systems of equations.

  • 33.
    Advaita, Advaita
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Gali, Mani Meghala
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Chu, Thi My Chinh
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Zepernick, Hans-Juergen
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies. Blekinge Inst Technol, SE-37179 Karlskrona, Sweden..
    Outage Probability of MIMO Cognitive Cooperative Radio Networks with Multiple AF Relays Using Orthogonal Space-Time Block Codes2017In: 2017 IEEE 13TH INTERNATIONAL CONFERENCE ON WIRELESS AND MOBILE COMPUTING, NETWORKING AND COMMUNICATIONS (WIMOB), IEEE , 2017, p. 84-89Conference paper (Refereed)
    Abstract [en]

    In this paper, we analyze the outage probability of multiple-input multiple-output cognitive cooperative radio networks (CCRNs) with multiple opportunistic amplify-and-forward relays. The CCRN applies underlay spectrum access accounting for the interference power constraint of a primary network and utilizes orthogonal space-time block coding to transmit multiple data streams across a number of antennas over several time slots. As such, the system exploits both time and space diversity to improve the transmission reliability over Nakagami.. fading. The CCRN applies opportunistic relaying in which the relay offering the highest signal-to-noise ratio at the receiver is selected to forward the transmit signal. Furthermore, selection combining is adopted at the secondary receiver to process the signal from the direct and relaying transmissions. To evaluate system performance, we derive an expression for the outage probability which is valid for an arbitrary number of antennas at the source, relays, and receiver of the CCRN. Selected numerical results are provided using Mathematica for analysis and Matlab for simulations, to reveal the effect of network parameters on the outage probability of the system.

  • 34. Afzal, Wasif
    Lessons from applying experimentation in software engineering prediction systems2008Conference paper (Refereed)
    Abstract [en]

    Within software engineering prediction systems, experiments are undertaken primarliy to investigate relationships and to measure/compare models' accuracy. This paper discusses our experience and presents useful lessons/guidelines in experimenting with software engineering prediction systems. For this purpose, we use a typical software engineering experimentation process as a baseline. We found that the typical software engineering experimentation process in software engineering is supportive in developing prediction systems and have highlighted issues more central to the domain of software engineering prediction systems.

  • 35. Afzal, Wasif
    Search-based approaches to software fault prediction and software testing2009Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Software verification and validation activities are essential for software quality but also constitute a large part of software development costs. Therefore efficient and cost-effective software verification and validation activities are both a priority and a necessity considering the pressure to decrease time-to-market and intense competition faced by many, if not all, companies today. It is then perhaps not unexpected that decisions related to software quality, when to stop testing, testing schedule and testing resource allocation needs to be as accurate as possible. This thesis investigates the application of search-based techniques within two activities of software verification and validation: Software fault prediction and software testing for non-functional system properties. Software fault prediction modeling can provide support for making important decisions as outlined above. In this thesis we empirically evaluate symbolic regression using genetic programming (a search-based technique) as a potential method for software fault predictions. Using data sets from both industrial and open-source software, the strengths and weaknesses of applying symbolic regression in genetic programming are evaluated against competitive techniques. In addition to software fault prediction this thesis also consolidates available research into predictive modeling of other attributes by applying symbolic regression in genetic programming, thus presenting a broader perspective. As an extension to the application of search-based techniques within software verification and validation this thesis further investigates the extent of application of search-based techniques for testing non-functional system properties. Based on the research findings in this thesis it can be concluded that applying symbolic regression in genetic programming may be a viable technique for software fault prediction. We additionally seek literature evidence where other search-based techniques are applied for testing of non-functional system properties, hence contributing towards the growing application of search-based techniques in diverse activities within software verification and validation.

  • 36. Afzal, Wasif
    Search-Based Prediction of Software Quality: Evaluations and Comparisons2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Software verification and validation (V&V) activities are critical for achieving software quality; however, these activities also constitute a large part of the costs when developing software. Therefore efficient and effective software V&V activities are both a priority and a necessity considering the pressure to decrease time-to-market and the intense competition faced by many, if not all, companies today. It is then perhaps not unexpected that decisions that affects software quality, e.g., how to allocate testing resources, develop testing schedules and to decide when to stop testing, needs to be as stable and accurate as possible. The objective of this thesis is to investigate how search-based techniques can support decision-making and help control variation in software V&V activities, thereby indirectly improving software quality. Several themes in providing this support are investigated: predicting reliability of future software versions based on fault history; fault prediction to improve test phase efficiency; assignment of resources to fixing faults; and distinguishing fault-prone software modules from non-faulty ones. A common element in these investigations is the use of search-based techniques, often also called metaheuristic techniques, for supporting the V&V decision-making processes. Search-based techniques are promising since, as many problems in real world, software V&V can be formulated as optimization problems where near optimal solutions are often good enough. Moreover, these techniques are general optimization solutions that can potentially be applied across a larger variety of decision-making situations than other existing alternatives. Apart from presenting the current state of the art, in the form of a systematic literature review, and doing comparative evaluations of a variety of metaheuristic techniques on large-scale projects (both industrial and open-source), this thesis also presents methodological investigations using search-based techniques that are relevant to the task of software quality measurement and prediction. The results of applying search-based techniques in large-scale projects, while investigating a variety of research themes, show that they consistently give competitive results in comparison with existing techniques. Based on the research findings, we conclude that search-based techniques are viable techniques to use in supporting the decision-making processes within software V&V activities. The accuracy and consistency of these techniques make them important tools when developing future decision-support for effective management of software V&V activities.

  • 37. Afzal, Wasif
    Using faults-slip-through metric as a predictor of fault-proneness2010Conference paper (Refereed)
    Abstract [en]

    The majority of software faults are present in small number of modules, therefore accurate prediction of fault-prone modules helps improve software quality by focusing testing efforts on a subset of modules. This paper evaluates the use of the faults-slip-through (FST) metric as a potential predictor of fault-prone modules. Rather than predicting the fault-prone modules for the complete test phase, the prediction is done at the specific test levels of integration and system test. We applied eight classification techniques to the task of identifying fault-prone modules, representing a variety of approaches, including a standard statistical technique for classification (logistic regression), tree-structured classifiers (C4.5 and random forests), a Bayesian technique (Na\"{i}ve Bayes), machine-learning techniques (support vector machines and back-propagation artificial neural networks) and search-based techniques (genetic programming and artificial immune recognition systems) on FST data collected from two large industrial projects from the telecommunication domain. \emph{Results:} Using area under the receiver operating characteristic (ROC) curve and the location of (PF, PD) pairs in the ROC space, GP showed impressive results in comparison with other techniques for predicting fault-prone modules at both integration and system test levels. The use of faults-slip-through metric in general provided good prediction results at the two test levels. The accuracy of GP is statistically significant in comparison with majority of the techniques for predicting fault-prone modules at integration and system test levels. (ii) Faults-slip-through metric has the potential to be a generally useful predictor of fault-proneness at integration and system test levels.

  • 38. Afzal, Wasif
    et al.
    Ghazi, Ahmad Nauman
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Itkonen, Juha
    Torkar, Richard
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Andrews, Anneliese
    Bhatti, Khurram
    An experiment on the effectiveness and efficiency of exploratory testing2015In: Empirical Software Engineering, ISSN 1382-3256, Vol. 20, no 3, p. 844-878Article in journal (Refereed)
    Abstract [en]

    The exploratory testing (ET) approach is commonly applied in industry, but lacks scientific research. The scientific community needs quantitative results on the performance of ET taken from realistic experimental settings. The objective of this paper is to quantify the effectiveness and efficiency of ET vs. testing with documented test cases (test case based testing, TCT). We performed four controlled experiments where a total of 24 practitioners and 46 students performed manual functional testing using ET and TCT. We measured the number of identified defects in the 90-minute testing sessions, the detection difficulty, severity and types of the detected defects, and the number of false defect reports. The results show that ET found a significantly greater number of defects. ET also found significantly more defects of varying levels of difficulty, types and severity levels. However, the two testing approaches did not differ significantly in terms of the number of false defect reports submitted. We conclude that ET was more efficient than TCT in our experiment. ET was also more effective than TCT when detection difficulty, type of defects and severity levels are considered. The two approaches are comparable when it comes to the number of false defect reports submitted.

  • 39. Afzal, Wasif
    et al.
    Torkar, Richard
    A Comparative Evaluation of Using Genetic Programming for Predicting Fault Count Data2008Conference paper (Refereed)
    Abstract [en]

    There have been a number of software reliability growth models (SRGMs) proposed in literature. Due to several reasons, such as violation of models' assumptions and complexity of models, the practitioners face difficulties in knowing which models to apply in practice. This paper presents a comparative evaluation of traditional models and use of genetic programming (GP) for modeling software reliability growth based on weekly fault count data of three different industrial projects. The motivation of using a GP approach is its ability to evolve a model based entirely on prior data without the need of making underlying assumptions. The results show the strengths of using GP for predicting fault count data.

  • 40.
    Afzal, Wasif
    et al.
    Blekinge Institute of Technology, School of Engineering, Department of Systems and Software Engineering.
    Torkar, Richard
    Blekinge Institute of Technology, School of Engineering, Department of Systems and Software Engineering.
    Incorporating Metrics in an Organizational Test Strategy2008Conference paper (Refereed)
    Abstract [en]

    An organizational level test strategy needs to incorporate metrics to make the testing activities visible and available to process improvements. The majority of testing measurements that are done are based on faults found in the test execution phase. In contrast, this paper investigates metrics to support software test planning and test design processes. We have assembled metrics in these two process types to support management in carrying out evidence-based test process improvements and to incorporate suitable metrics as part of an organization level test strategy. The study is composed of two steps. The first step creates a relevant context by analyzing key phases in the software testing lifecycle, while the second step identifies the attributes of software test planning and test design processes along with metric(s) support for each of the identified attributes.

  • 41. Afzal, Wasif
    et al.
    Torkar, Richard
    On the application of genetic programming for software engineering predictive modeling: A systematic review2011In: Expert Systems with Applications, ISSN 0957-4174 , Vol. 38, no 9, p. 11984-11997Article, review/survey (Refereed)
    Abstract [en]

    The objective of this paper is to investigate the evidence for symbolic regression using genetic programming (GP) being an effective method for prediction and estimation in software engineering, when compared with regression/machine learning models and other comparison groups (including comparisons with different improvements over the standard GP algorithm). We performed a systematic review of literature that compared genetic programming models with comparative techniques based on different independent project variables. A total of 23 primary studies were obtained after searching different information sources in the time span 1995-2008. The results of the review show that symbolic regression using genetic programming has been applied in three domains within software engineering predictive modeling: (i) Software quality classification (eight primary studies). (ii) Software cost/effort/size estimation (seven primary studies). (iii) Software fault prediction/software reliability growth modeling (eight primary studies). While there is evidence in support of using genetic programming for software quality classification, software fault prediction and software reliability growth modeling: the results are inconclusive for software cost/effort/size estimation.

  • 42. Afzal, Wasif
    et al.
    Torkar, Richard
    Suitability of Genetic Programming for Software Reliability Growth Modeling2008Conference paper (Refereed)
    Abstract [en]

    Genetic programming (GP) has been found to be effective in finding a model that fits the given data points without making any assumptions about the model structure. This makes GP a reasonable choice for software reliability growth modeling. This paper discusses the suitability of using GP for software reliability growth modeling and highlights the mechanisms that enable GP to progressively search for fitter solutions.

  • 43. Afzal, Wasif
    et al.
    Torkar, Richard
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Towards benchmarking feature subset selection methods for software fault prediction2016In: Studies in Computational Intelligence, Springer, 2016, 617, Vol. 617, p. 33-58Chapter in book (Refereed)
    Abstract [en]

    Despite the general acceptance that software engineering datasets often contain noisy, irrelevant or redundant variables, very few benchmark studies of feature subset selection (FSS) methods on real-life data from software projects have been conducted. This paper provides an empirical comparison of state-of-the-art FSS methods: information gain attribute ranking (IG); Relief (RLF); principal component analysis (PCA); correlation-based feature selection (CFS); consistencybased subset evaluation (CNS); wrapper subset evaluation (WRP); and an evolutionary computation method, genetic programming (GP), on five fault prediction datasets from the PROMISE data repository. For all the datasets, the area under the receiver operating characteristic curve—the AUC value averaged over 10-fold cross-validation runs—was calculated for each FSS method-dataset combination before and after FSS. Two diverse learning algorithms, C4.5 and naïve Bayes (NB) are used to test the attribute sets given by each FSS method. The results show that although there are no statistically significant differences between the AUC values for the different FSS methods for both C4.5 and NB, a smaller set of FSS methods (IG, RLF, GP) consistently select fewer attributes without degrading classification accuracy. We conclude that in general, FSS is beneficial as it helps improve classification accuracy of NB and C4.5. There is no single best FSS method for all datasets but IG, RLF and GP consistently select fewer attributes without degrading classification accuracy within statistically significant boundaries. © Springer International Publishing Switzerland 2016.

  • 44. Afzal, Wasif
    et al.
    Torkar, Richard
    Feldt, Robert
    A Systematic Mapping Study on Non-Functional Search-Based Software Testing2008Conference paper (Refereed)
  • 45. Afzal, Wasif
    et al.
    Torkar, Richard
    Feldt, Robert
    A systematic review of search-based testing for non-functional system properties2009In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 51, no 6, p. 957-976Article in journal (Refereed)
    Abstract [en]

    Search-based software testing is the application of metaheuristic search techniques to generate software tests. The test adequacy criterion is transformed into a fitness function and a set of solutions in the search space are evaluated with respect to the fitness function using a metaheuristic search technique. The application of metaheuristic search techniques for testing is promising due to the fact that exhaustive testing is infeasible considering the size and complexity of software under test. Search-based software testing has been applied across the spectrum of test case design methods; this includes white-box (structural), black-box (functional) and grey-box (combination of structural and functional) testing. In addition, metaheuristic search techniques have also been applied to test non-functional properties. The overall objective of undertaking this systematic review is to examine existing work into non-functional search-based software testing (NFSBST). We are interested in types of non-functional testing targeted using metaheuristic search techniques, different fitness functions used in different types of search-based non-functional testing and challenges in the application of these techniques. The systematic review is based on a comprehensive set of 35 articles obtained after a multi-stage selection process and have been published in the time span 1996-2007. The results of the review show that metaheuristic search techniques have been applied for non-functional testing of execution time, quality of service, security, usability and safety. A variety of metaheuristic search techniques are found to be applicable for non-functional testing including simulated annealing, tabu search, genetic algorithms, ant colony methods, grammatical evolution, genetic programming (and its variants including linear genetic programming) and swarm intelligence methods. The review reports on different fitness functions used to guide the search for each of the categories of execution time, safety, usability, quality of service and security; along with a discussion of possible challenges in the application of metaheuristic search techniques.

  • 46. Afzal, Wasif
    et al.
    Torkar, Richard
    Feldt, Robert
    Prediction of fault count data using genetic programming2008Conference paper (Refereed)
    Abstract [en]

    Software reliability growth modeling helps in deciding project release time and managing project resources. A large number of such models have been presented in the past. Due to the existence of many models, the models' inherent complexity, and their accompanying assumptions; the selection of suitable models becomes a challenging task. This paper presents empirical results of using genetic programming (GP) for modeling software reliability growth based on weekly fault count data of three different industrial projects. The goodness of fit (adaptability) and predictive accuracy of the evolved model is measured using five different measures in an attempt to present a fair evaluation. The results show that the GP evolved model has statistically significant goodness of fit and predictive accuracy.

  • 47.
    Afzal, Wasif
    et al.
    Blekinge Institute of Technology, School of Computing.
    Torkar, Richard
    Blekinge Institute of Technology, School of Computing.
    Feldt, Robert
    Blekinge Institute of Technology, School of Computing.
    Resampling Methods in Software Quality Classification2012In: International Journal of Software Engineering and Knowledge Engineering, ISSN 0218-1940, Vol. 22, no 2, p. 203-223Article in journal (Refereed)
    Abstract [en]

    In the presence of a number of algorithms for classification and prediction in software engineering, there is a need to have a systematic way of assessing their performances. The performance assessment is typically done by some form of partitioning or resampling of the original data to alleviate biased estimation. For predictive and classification studies in software engineering, there is a lack of a definitive advice on the most appropriate resampling method to use. This is seen as one of the contributing factors for not being able to draw general conclusions on what modeling technique or set of predictor variables are the most appropriate. Furthermore, the use of a variety of resampling methods make it impossible to perform any formal meta-analysis of the primary study results. Therefore, it is desirable to examine the influence of various resampling methods and to quantify possible differences. Objective and method: This study empirically compares five common resampling methods (hold-out validation, repeated random sub-sampling, 10-fold cross-validation, leave-one-out cross-validation and non-parametric bootstrapping) using 8 publicly available data sets with genetic programming (GP) and multiple linear regression (MLR) as software quality classification approaches. Location of (PF, PD) pairs in the ROC (receiver operating characteristics) space and area under an ROC curve (AUC) are used as accuracy indicators. Results: The results show that in terms of the location of (PF, PD) pairs in the ROC space, bootstrapping results are in the preferred region for 3 of the 8 data sets for GP and for 4 of the 8 data sets for MLR. Based on the AUC measure, there are no significant differences between the different resampling methods using GP and MLR. Conclusion: There can be certain data set properties responsible for insignificant differences between the resampling methods based on AUC. These include imbalanced data sets, insignificant predictor variables and high-dimensional data sets. With the current selection of data sets and classification techniques, bootstrapping is a preferred method based on the location of (PF, PD) pair data in the ROC space. Hold-out validation is not a good choice for comparatively smaller data sets, where leave-one-out cross-validation (LOOCV) performs better. For comparatively larger data sets, 10-fold cross-validation performs better than LOOCV.

  • 48. Afzal, Wasif
    et al.
    Torkar, Richard
    Feldt, Robert
    Search-based prediction of fault count data2009Conference paper (Refereed)
    Abstract [en]

    Symbolic regression, an application domain of genetic programming (GP), aims to find a function whose output has some desired property, like matching target values of a particular data set. While typical regression involves finding the coefficients of a pre-defined function, symbolic regression finds a general function, with coefficients, fitting the given set of data points. The concepts of symbolic regression using genetic programming can be used to evolve a model for fault count predictions. Such a model has the advantages that the evolution is not dependent on a particular structure of the model and is also independent of any assumptions, which are common in traditional time-domain parametric software reliability growth models. This research aims at applying experiments targeting fault predictions using genetic programming and comparing the results with traditional approaches to compare efficiency gains.

  • 49. Afzal, Wasif
    et al.
    Torkar, Richard
    Feldt, Robert
    Gorschek, Tony
    Genetic programming for cross-release fault count predictions in large and complex software projects2010In: Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques / [ed] Chis, Monica, Hershey: IGI Global, Hershey, USA , 2010Chapter in book (Refereed)
    Abstract [en]

    Software fault prediction can play an important role in ensuring software quality through efficient resource allocation. This could, in turn, reduce the potentially high consequential costs due to faults. Predicting faults might be even more important with the emergence of short-timed and multiple software releases aimed at quick delivery of functionality. Previous research in software fault prediction has indicated that there is a need i) to improve the validity of results by having comparisons among number of data sets from a variety of software, ii) to use appropriate model evaluation measures and iii) to use statistical testing procedures. Moreover, cross-release prediction of faults has not yet achieved sufficient attention in the literature. In an attempt to address these concerns, this paper compares the quantitative and qualitative attributes of 7 traditional and machine-learning techniques for modeling the cross-release prediction of fault count data. The comparison is done using extensive data sets gathered from a total of 7 multi-release open-source and industrial software projects. These software projects together have several years of development and are from diverse application areas, ranging from a web browser to a robotic controller software. Our quantitative analysis suggests that genetic programming (GP) tends to have better consistency in terms of goodness of fit and accuracy across majority of data sets. It also has comparatively less model bias. Qualitatively, ease of configuration and complexity are less strong points for GP even though it shows generality and gives transparent models. Artificial neural networks did not perform as well as expected while linear regression gave average predictions in terms of goodness of fit and accuracy. Support vector machine regression and traditional software reliability growth models performed below average on most of the quantitative evaluation criteria while remained on average for most of the qualitative measures.

  • 50. Afzal, Wasif
    et al.
    Torkar, Richard
    Blekinge Institute of Technology, School of Computing.
    Feldt, Robert
    Blekinge Institute of Technology, School of Computing.
    Gorschek, Tony
    Blekinge Institute of Technology, School of Computing.
    Prediction of faults-slip-through in large software projects: an empirical evaluation2014In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367, Vol. 22, no 1, p. 51-86Article in journal (Refereed)
    Abstract [en]

    A large percentage of the cost of rework can be avoided by finding more faults earlier in a software test process. Therefore, determination of which software test phases to focus improvement work on has considerable industrial interest. We evaluate a number of prediction techniques for predicting the number of faults slipping through to unit, function, integration, and system test phases of a large industrial project. The objective is to quantify improvement potential in different test phases by striving toward finding the faults in the right phase. The results show that a range of techniques are found to be useful in predicting the number of faults slipping through to the four test phases; however, the group of search-based techniques (genetic programming, gene expression programming, artificial immune recognition system, and particle swarm optimization-based artificial neural network) consistently give better predictions, having a representation at all of the test phases. Human predictions are consistently better at two of the four test phases. We conclude that the human predictions regarding the number of faults slipping through to various test phases can be well supported by the use of search-based techniques. A combination of human and an automated search mechanism (such as any of the search-based techniques) has the potential to provide improved prediction results.

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