The quality of the test suites and the constituent test cases significantly impacts confidence in software testing. While research has identified several quality attributes of test cases and test suites, there is a need for a better understanding of their relative importance in practice. We investigate practitioners' perceptions regarding the relative importance of quality attributes of test cases and test suites and the challenges that they face in ensuring the perceived important quality attributes. To capture the practitioners' perceptions, we conducted an industrial survey using a questionnaire based on the quality attributes identified in an extensive literature review. We used a sampling strategy that leverages LinkedIn to draw a large and heterogeneous sample of professionals with experience in software testing. We collected 354 responses from practitioners with a wide range of experience (from less than one year to 42 years of experience). We found that the majority of practitioners rated Fault Detection, Usability, Maintainability, Reliability, and Coverage to be the most important quality attributes. Resource Efficiency, Reusability, and Simplicity received the most divergent opinions, which, according to our analysis, depend on the software-testing contexts. Also, we identified common challenges that apply to the important attributes, namely inadequate definition, lack of useful metrics, lack of an established review process, and lack of external support. The findings point out where practitioners actually need further support with respect to achieving high-quality test cases and test suites under different software testing contexts. Hence, the findings can serve as a guideline for academic researchers when looking for research directions on the topic. Furthermore, the findings can be used to encourage companies to provide more support to practitioners to achieve high-quality test cases and test suites.

This study presented a decision support system based on data mining and machine learning techniques for accurate prediction of cardiac patients' deaths because resources in the hospitals are limited; therefore, appropriate allocation of resources will improve the survivability of cardiac patients. Data mining techniques are widely used by researchers to uncover hidden information and patterns that could potentially save or prolong patient lives. Age, gender, high blood pressure, cholesterol, and irregular heartbeat rates are some of the variables considered for this study. This study used medical health records data from 368 observations with 55 unique features. This work presents a machine learning-based approach for predicting cardiac patient death by employing an electronic health record (EHR) dataset. The constructed model is based on a genetic algorithm (GA) for selecting important features from the dataset and a Random Forest (RF) model for classifying mortality in cardiac patients. The hyperparameters of RF models were optimized using a grid search algorithm for improved performance of RF. A public dataset was obtained in order to evaluate the efficacy of the constructed GA_RF model. One of the problems that we encountered during this study was imbalance classes in the collected dataset. The machine learning models tend to bias toward the majority class in the dataset. To overcome this problem, the Random Under Sampling (RUS) method was employed. The performance of the constructed GA_RF model was tested on several evaluation metrics, and results validate the effectiveness of the proposed GA_RF model for mortality prediction in cardiac patients.

This study examines the challenges and students’perceptions of adopting and implementing e-learning in Tanzanian universities to facilitate the shift towards personalised learning. Although technology and online courses provide chances to customise training for unique student requirements, challenges impede the successful incorporation of e-learning in Tanzanian higher education, particularly regarding user training and technical support. The absence of crucial amenities for online education, such as computer laboratories and dependable internet connectivity, exacerbates the challenges in executing implementation endeavours.

We employed a mixed-methods approach to investigate universities’preparedness for implementing e-learning and evaluate the impact of e-learning platforms and Artificial Intelligence(AI) in facilitating personalised learning experiences.

Our findings emphasise the significance of tackling identified obstacles to enhance education quality and provide a basis for customised learning. Subsequent studies should focus on filling the existing voids to improve the incorporation of artificial intelligence in e-learning platforms, explicitly addressing the obstacles faced by universities in underdeveloped nations. The study suggests that institutions should allocate resources towards developing e-learning infrastructure, offer extensive training for instructors, and create customised e-learning methods.

Rapid urbanisation has intensified the need for sustainable solutions to address challenges in urban infrastructure, climate change, and resource constraints. This study reveals that Artificial Intelligence (AI)-enabled metaverse offers transformative potential for developing sustainable smart cities. AI techniques, such as machine learning, deep learning, generative AI (GAI), and large language models (LLMs), enhance the metaverse’s capabilities in data analysis, urban decision making, and personalised user experiences. The study further examines how these advanced AI models facilitate key metaverse technologies such as big data analytics, natural language processing (NLP), computer vision, digital twins, Internet of Things (IoT), Edge AI, and 5G/6G networks. Applications across various smart city domains—environment, mobility, energy, health, governance, and economy, and real-world use cases of virtual cities like Singapore, Seoul, and Lisbon are presented, demonstrating AI’s effectiveness in the metaverse for smart cities. However, AI-enabled metaverse in smart cities presents challenges related to data acquisition and management, privacy, security, interoperability, scalability, and ethical considerations. These challenges’ societal and technological implications are discussed, highlighting the need for robust data governance frameworks and AI ethics guidelines. Future directions emphasise advancing AI model architectures and algorithms, enhancing privacy and security measures, promoting ethical AI practices, addressing performance measures, and fostering stakeholder collaboration. By addressing these challenges, the full potential of AI-enabled metaverse can be harnessed to enhance sustainability, adaptability, and livability in smart cities. 

This work presents four sets of welded multi-wires on Cu sheet (e.g., A-(95/95mm2), B-(35, 35, 25/35, 35, 25 mm2), C-(50, 35, 10/50, 35, 10 mm2), D-(35, 35, 25/50, 35, 10 mm2)) using ultrasonic wire harness welding (USWW). Forming quality of multi-wire joint, examination of microstructures, root gaps influence, fractographic analysis and surface micro-topography were investigated and clarified. Tensile strength and microhardness of samples in B-(35, 35, 25/35, 35, 25 mm2) increases up to peak load (6527.7 N and 484.5 HV) under welding energy of 1000 J. Tensile strength of B-(35, 35, 25/35, 35, 25 mm2/(6527.7 N)) is 29.2 % higher than A-(95/95mm2/(3681 N)) under effect of root gap. Maximum hardness of Cu plate in group B was obtained 484.5HV and maximum hardness of sample reached 363.2HV and 251HV in group C and A respectively. Stability of sample in group B is generally increased by 20.5 % compared to A3, C5 and D2. Failure displacement of joint A3 is 35 % higher than joint B1. Failure displacement of joint C5 is 15 % higher than joint D2. EDS analysis experienced Cu content at welded interface is higher compared to rest of locations which generates more fine grains structures to increase strength and weldability of joints. Kathmatic microscope investigated microtopographical features in group B sample wherein regular surface size was measured to be approximately 10μm. A standard root gap allows welded sheet to penetrate amid multi-wires being joined tightly and ensures a strong bond in group B. © 2024 The Authors

This study proposes a zero-inflated Rayleigh seasonal autoregressive moving average model with exogenous regressors (iRSARMAX) to model and forecast non-negative time series, accommodating the presence of zero values. The proposed iRSARMAX models the conditional mean of the continuous part of the mixture distribution by using a dynamic structure that considers stochastic seasonality, autoregressive and moving average terms, exogenous regressors, and a link function. It also models the mixture parameters related to the inflated (zero) values with a parsimonious dynamic structure. Furthermore, the analytical score vector was deduced and considered in the conditional maximum likelihood estimation of the introduced model parameters. The analytical Fisher information matrix was obtained and used for hypothesis testing and interval inferences for the parameters of the proposed model. Randomized quantile residuals were considered, and goodness-of-fit tests were implemented to validate the model. An extensive simulation study was performed to evaluate the performance of conditional likelihood inference over the model parameters for finite sample sizes. The proposed model excelled compared to the traditional seasonal autoregressive and moving average model and the Holt-Winters filtering in forecasting influent flow. In addition, it outperformed competitors in predicting synthetic aperture radar (SAR) image data. 

Preventing customer churn, i.e., termination of business commitments, is essential for companies operating in saturated markets, especially for subscription-based models such as telecommunication. Knowing when customers decide to terminate services is instrumental to effective churn prevention. In this study, we investigate how churn prediction performs in practice when training models on different time intervals of historic data (1-4 weeks back) and predicting churn at different numbers of weeks ahead (1-4 weeks). We use a real-world, time-series dataset of mobile subscription usage to examine churn prediction for business-to-business (B2B) customers. We utilize the timeseries data at a higher temporal resolution than prior studies and investigate different forecasting horizons. Leveraging popular machine learning algorithms such as Random Forests, Gradient Boosting, Neural Networks, and Gated Recurrent Unit, we show that the best model achieves an average F1-score of 79.3% for one-week ahead predictions. However, the average F1-score decreases to 63.3% and 61.8% for two and four weeks ahead, respectively. A model interpretation framework (SHAP) evaluates the feature impact on the models' internal decision logic. We also discuss the challenges in applying churn prediction for the B2B segment. 

Contemporary practices such as InnerSource (adopting open source practices within an organization), continuous integration and delivery (CI/CD), and the use of microservices promote software reuse. Although the benefits of individual contemporary practices on reuse may be known, the implications of collective contemporary practices, mainly challenges and improvements to mitigate the challenges, are to a large extent unknown. In this study, we investigate the additional effort (cost factors), benefits, challenges, and potential improvements in contemporary reuse at Ericsson.

We performed the study in two phases: a) the initial data collection based on a combination of data collection methods (e.g., interviews, discussions, company portals), and b) a follow-up group discussion after a year to understand the status of the challenges and improvements identified in the first phase.

Our results indicate that developing reusable assets resulted in upfront cost factors, such as additional effort in ensuring compliance. Furthermore, development with reuse also resulted in cost factors, for example, additional effort in integrating and understanding reusable assets. Ericsson perceived the cost factors as an investment resulting in long-term benefits such as improved quality, productivity, customer experience, and way of working. The main challenge faced by Ericsson was the pressure on the producers of reusable assets. Our study shows how InnerSource can lead to an increase in contributions to reusable assets. Furthermore, Ericsson implemented measures like automating compliance checks that improved the maturity of reusable assets, resulting in an increase in their reuse.

In summary, effective use of contemporary practices such as InnerSource and CI/CD, along with the use of microservices, can facilitate large scale reuse. © The Author(s) 2024.

Context: In software development organizations employing weak or collective ownership, different teams are allowed and expected to autonomously perform changes in various components. This creates diversity both in the knowledge of, and in the responsibility for, individual components.

Objective: Our objective is to understand how and why different teams introduce technical debt in the form of code clones as they change different components.

Method: We collected data about change size and clone introductions made by ten teams in eight components which was part of a large industrial software system. We then designed a Multi-Level Generalized Linear Model (MLGLM), to illustrate the teams’ differing behavior. Finally, we discussed the results with three development teams, plus line manager and the architect team, evaluating whether the model inferences aligned with what they expected. Responses were recorded and thematically coded.

Results: The results show that teams do behave differently in different components, and the feedback from the teams indicates that this method of illustrating team behavior can be useful as a complement to traditional summary statistics of ownership.

Conclusions: We find that our model-based approach produces useful visualizations of team introductions of code clones as they change different components. Practitioners stated that the visualizations gave them insights that were useful, and by comparing with an average team, inter-team comparisons can be avoided. Thus, this has the potential to be a useful feedback tool for teams in software development organizations that employ weak or collective ownership. © The Author(s) 2024.

Variance theories quantify the variance that one or more independent variables cause in a dependent variable. In software engineering (SE), variance theories are used toquantify—among others—the impact of tools, techniques, andother treatments on software development outcomes. To acquire variance theories, evidence from individual empirical studies needs to be synthesized to more generally valid conclusions. However, research synthesis in SE is mostly limited to meta-analysis, which requires homogeneity of the synthesized studies to infer generalizable variance. In this paper, we aim to extend the practice of research synthesis beyond meta-analysis. To this end, we derive a conceptual framework for the evolution of variance theories and demonstrate its use by applying it to an active research field in SE. The resulting framework allows researchers to put new evidence in a clear relation to an existing body of knowledge and systematically expand the scientific frontier of a studied phenomenon.

För att lyckas i arbetslivet är det viktigt att kunna samarbeta med andra människor på olika vis. För att lyckas som praktiserande planeringsarkitekt är det avgörande att kunna samarbeta effektivt med andra professioner, politiker och allmänheten (Davoudi 2010; Rooij & Frank 2016). Beebe och Masterson (2003) beskriver att färdigheter i grupparbeten är högt värderat hos arbetsgivare samtidigt som det också finns stora fördelar kopplat till gemensamt lärande. Våra studenters färdigheter i projektledning är också något som efterfrågas i allt högre utsträckning av våra externa kontakter, studenternas framtida yrkesgivare. Att arbeta i grupp stimulerar även kreativitet och diskussioner, varför det är en självklar del av både program- och kursinnehåll på Institutionen för fysisk planering.

In this presentation, I will summarize the work done to prepare and run a course module regarding Gender and Social Equality (Jämställdhet och Jämlikhet) offered to first-year students of a Bachelor of Engineering program (Högskoleingenjör i teknisk spelgrafik). The module has been developed as part of an introductory course (Game Development in Technical Game-graphics). The goal of this module is for the students to acquire basic knowledge on the topic and start relevant discussions about gender and social equality in relation to the industry the students are targeting with their study, in this case, the gaming industry and the world of digital games.

Education is not only about preparing students for a life in industry (reality), but also about moving the existing reality into the new normal by the hands of these fresh students. For more than 25 years we have created capstone project courses where industry (and society) have met the minds of students, and seen amazing results happen. Automotive, aerospace, medicine, healthcare, municipality; many are the application areas that our students from engineering have embarked upon.  Trust the process!

Bakgrund; I sjuksköterskeprogrammet ingår VFU omfattande 50% och stor del av dessa studier genomförs på sjukhus eller kommunal hälso- och sjukvård. I avtalet som skrivs med Region och Kommuner ställs krav på en handledarutbildning omfattande 7,5 hp. Det är ett krav som lyfts fram av UKÄ för att säkerställa en utbildning med god pedagogisk kvalitet. Den stora utmaningen är att få tillräckligt många handledare som har handledarkompetens, vilket från Region och Kommuner oftast hänvisas till hög arbetsbelastning som medför att det inte går att avsätta tid för personalens att gå kursen.  

Åtgärd; Framtagande och utformning av kursen Handledning inom verksamhetsförlagd utbildning - kompetens för handledningsuppdrag, med fokus på genomförande asynkront.

The digitalisation of tertiary education and interest in online learning have been growing for over a decade, making it a growth sector for universities. Patel et al. (2023) define online learning as education that involves the full capabilities of current technologies to provide an experience of immersed learning and meaningful communication among students and between students and instructors.  Students now request more flexible forms of blended or online distance education that suit their lifestyle and preferred learning method (Maguire et al., 2020).   To meet students' expectations, university lecturers must learn how to teach in an online, virtual environment and learn new methods for course design, teaching, communication, and assessment of students. This study aims to identify critical factors for online distance education by interviewing lecturers in a Swedish international online Master’s program in engineering. The results were analysed using the Community of Inquiry framework to identify critical factors in online distance education.

We must adapt to the new educational landscape shaped by Generative AI, especially Large Language Models like ChatGPT. This shift disrupts traditional learning models and requires a major redesign of learning activities. We also need a new evaluation framework to determine when using Generative AI in education is appropriate or even encouraged, and when it is prohibited. Instead of resisting the inevitable, we should clearly communicate what is acceptable and what is not.

Genom att gå in i en roll och debattera om ett specifikt ämne ökar förståelsen för

de olika synvinklarna vilket bidrar till en bredare kunskapsbild. Debatten uppmuntrar

även till ett ökat studentdeltagande i undervisningen och tränar studenterna i ett

kritiskt tänkande såväl som muntlig kommunikation. Men, för att debatten skall

uppnå detta krävs det en tydlig struktur både inför och under själva debatten.

Student engagement is a crucial determinant of success

in online education, yet it presents unique challenges. In

virtual environments, students may feel isolated and reluctant

to seek help. Research highlights strategies like interactive

tools and active learning pedagogies to enhance engagement,

but online course attrition remains high due to issues like low

motivation, minimal peer interaction, and a lack of perceived

competence. To combat these challenges, course design must

carefully cater to student needs, fostering autonomy,

competence, and relatedness to optimize engagement and

promote retention.

TIHA - Institutionen för hälsa har använt Inspera för tentamen sedan 2017

och genomför alla salstentamen digitalt. Inspera är den digitala provplattform

som används på BTH. Genom att digitalisera tentamen får man förutom ett

effektivare tentamensflöde även nya möjligheter att utforma tentamensfrågor

With the rise of Generative AI and Large Language Models I wanted to see

if these tools could be used to give students valuable feedback without taking

away teacher time.