Context: Investigating factors influencing agile team climate is pressing given its impact on organizational performance. Despite previous studies, association between human aspects and team climate remains yet unexplored. Objective: Exploring association between two human aspects, namely team member's capability measures and personality traits, and member's perceived team climate. Method: 75 professionals (12 teams) from one division participated in the first survey iteration, second iteration in another division included 46 professionals (7 teams). We employed correlation analyses to measure association between human aspects and climate, and regression analyses to identify team climate predictors. Results: In relation to team climate dimensions, we observed a significant negative correlation with neuroticism and a significant positive correlation with responsibility. Linear regression analysis showed capability measures accounted for 19.2% of variance in team climate. Multivariable regression analysis revealed capability measures and personality traits together accounted for 25.7% of variance in team climate. Conclusion: An individual's propensity towards self-doubt (neuroticism) negatively affects perceived team climate, whereas individual's ability to be responsible and teamwork-oriented positively affect perceived team climate. The inclusion of both capability and personality variables as input for multivariable regression explained slightly more variance in team climate, compared to only capability measures (25.7% against 19.2%). © 2024 The Author(s)

Context: Former literature revealed team performance is contingent on personality composition and interactive effects of team climate. While decades of research on personality prevails in software engineering, team climate remains sparsely researched. Objective: In agile software development, individuals and interactions are key sources of agility. This study replicates a previous study and analyzes the relationship between five-factor-model personality traits and team climate dimensions among agile teams in a telecom company. Method: A Web-based survey was replicated twice, first with 75 professionals from 12 teams in Sweden, followed by 46 professionals from seven teams in India. The data was used for correlation, regression analyses, and meta-analysis. Results: We observed significant negative correlations between neuroticism and all the team climate dimensions. Meta-analysis identified a significant medium-sized negative effect between neuroticism and participative safety. Regression analysis showed personality traits accounted for around 10 % of the variance in team climate dimensions. Conclusions: High neuroticism is not conducive to team climate as emotionally unstable members could impair team cohesion by being reactive and susceptible to stress. Managers assembling Scrum teams ought to mitigate higher neuroticism by counterbalancing it with an elevation of corresponding negatively correlated personality variables and providing support/training towards increasing the aforementioned variables. © 2023

Context: A study in 2020 (S1) explored the relationship between personality traits and team climate perceptions of software professionals working in agile teams. S1 surveyed 43 software professionals from a large telecom company in Sweden and found that a person's ability to get along with team members (Agreeableness) influences significantly and positively the perceived level of team climate. Further, they observed that personality traits accounted for less than 15 % of the variance in team climate. Objective: The study described herein replicates S1 using data gathered from 148 software professionals from an industrial partner in Brazil. Method: We used the same research methods as S1. We employed a survey to gather the personality and climate data, which was later analyzed using correlation and regression analyses. The former aimed to measure the level of association between personality traits and climate and the latter to estimate team climate factors using personality traits as predictors. Results: The results for the correlation analyses showed statistically significant and positive associations between two personality traits - Agreeableness and Conscientiousness, and all five team climate factors. There was also a significant and positive association between Openness and Team Vision. Our results corroborate those from S1, with respect to two personality traits – Openness and Agreeableness; however, in S1, Openness was significantly and positively associated with Support for Innovation (not Team Vision). In regard to Agreeableness, in S1 it was also significantly and positively associated with perceived team climate. Furthermore, our regression models also support S1’s findings - personality traits accounted for less than 15 % of the variance in team climate. Conclusion: Despite variances in location, sample size, and operational domain, our study confirmed S1′s results on the limited influence of personality traits. Agreeableness and Openness were significant predictors for team climate, although the predictive factors differed. These discrepancies highlight the necessity for further research, incorporating larger samples and additional predictor variables, to better comprehend the intricate relationship between personality traits and team climate across diverse cultural and professional settings. © 2024 Elsevier B.V.

Context: Research quality is intended to appraise the design and reporting of studies. It comprises a set of standards such as methodological rigor, practical relevance, and conformance to ethical standards. Depending on the perspective, different views of importance are given to the standards for research quality. Objective: To investigate the suitability of a conceptual model of research quality to Software Engineering (SE), from the perspective of researchers engaged in Empirical Software Engineering (ESE) research, in order to understand the core value of research quality. Method: We conducted a mixed-methods approach with two distinct group perspectives: (i) a research group; and (ii) the empirical SE research community. Our data collection approach comprised a questionnaire survey and a complementary focus group. We carried out a hierarchical voting prioritization to collect relative values for importance of standards for research quality. Results: In the context of this research, ‘internally valid’, ‘relevant research idea’, and ‘applicable results’ are perceived as the core standards for research quality in empirical SE. The alignment at the research group level was higher compared to that at the community level. Conclusion: The conceptual model was seen to express fairly the standards for research quality in the SE context. It presented limitations regarding its structure and components’ description, which resulted in an updated model. © 2022

During the last decade, we have witnessed a rapiddevelopment of extended reality (XR) technologies such asaugmented reality (AR) and virtual reality (VR). Further, therehave been tremendous advancements in artificial intelligence(AI) and machine learning (ML). These two trends will havea significant impact on future digital societies. The vision ofan immersive, ubiquitous, and intelligent virtual space opensup new opportunities for creating an enhanced digital world inwhich the users are at the center of the development process,so-calledintelligent realities(IRs).The “Human-Centered Intelligent Realities” (HINTS) profileproject will develop concepts, principles, methods, algorithms,and tools for human-centered IRs, thus leading the wayfor future immersive, user-aware, and intelligent interactivedigital environments. The HINTS project is centered aroundan ecosystem combining XR and communication paradigms toform novel intelligent digital systems.HINTS will provide users with new ways to understand,collaborate with, and control digital systems. These novelways will be based on visual and data-driven platforms whichenable tangible, immersive cognitive interactions within realand virtual realities. Thus, exploiting digital systems in a moreefficient, effective, engaging, and resource-aware condition.Moreover, the systems will be equipped with cognitive featuresbased on AI and ML, which allow users to engage with digitalrealities and data in novel forms. This paper describes theHINTS profile project and its initial results. ©2023, Copyright held by the authors   

This document contains the supplementary material of the paper titled: "Examining the effect of software professionals’ capability and personality on agile team climate" 

Background: Integrating value-oriented perspectives into the principles and practices of software engineering is fundamental to ensure that software development activities address key stakeholders’ views and also balance short-and long-term goals. This is put forward in the discipline of value-based software engineering (VBSE) Aim: This study aims to provide an overview of VBSE with respect to the research efforts that have been put into VBSE. Method: We conducted a systematic mapping study to classify evidence on value definitions, studies’ quality, VBSE principles and practices, research topics, methods, types, contribution facets, and publication venues. Results: From 143 studies we found that the term “value” has not been clearly defined in many studies. VB Requirements Engineering and VB Planning and Control were the two principles mostly investigated, whereas VB Risk Management and VB People Management were the least researched. Most studies showed very good reporting and relevance quality, acceptable credibility, but poor in rigour. Main research topic was Software Requirements and case study research was the method used the most. The majority of studies contribute towards methods and processes, while very few studies have proposed metrics and tools. Conclusion: We highlighted the research gaps and implications for research and practice to support VBSE. © 2023 The Authors.

Value-Based Software Engineering stresses the role of value in software related decisions. In the context of feature selection, software features judged to provide higher value take priority in the development process. This paper focuses on what value means when selecting software features. Using grounded theory, we conducted and analyzed semi-structured interviews with 21 key stakeholders (decision-makers) from three software/software-intensive companies, within a context where value-based decision-making was already established. Our analysis led to the building of a theory of value for value-based feature selection that identifies the nature of value propositions considered by key stakeholders when selecting software features (i.e., decision-making criteria for deciding upon software features, as suggested by Boehm (2003)). We found that some value propositions were common to all three company cases (core value propositions), whereas others were dependent upon the context in which a company operates, and the characteristics of the product under development (specific value propositions). Moreover, value propositions vary according to the stakeholder group and the type of feature being assessed. Our study provides significant insight into value in the context of feature selection, and generates new concepts around value-based feature selection such as new value propositions.

Background: A good search strategy is essential for a successful systematic literature study. Historically, database searches have been the norm, which was later complemented with snowball searches. Our conjecture is that we can perform even better searches if combining these two search approaches, referred to as a hybrid search strategy. Objective: Our main objective was to compare and evaluate a hybrid search strategy. Furthermore, we compared four alternative hybrid search strategies to assess whether we could identify more cost-efficient ways of searching for relevant primary studies. Methods: To compare and evaluate the hybrid search strategy, we replicated the search procedure in a systematic literature review (SLR) on industry–academia collaboration in software engineering. The SLR used a more “traditional” approach to searching for relevant articles for an SLR, while our replication was executed using a hybrid search strategy. Results: In our evaluation, the hybrid search strategy was superior in identifying relevant primary studies. It identified 30% more primary studies and even more studies when focusing only on peer-reviewed articles. To embrace individual viewpoints when assessing research articles and minimise the risk of missing primary studies, we introduced two new concepts, wild cards and borderline articles, when performing systematic literature studies. Conclusions: The hybrid search strategy is a strong contender for being used when performing systematic literature studies. Furthermore, alternative hybrid search strategies may be viable if selected wisely in relation to the start set for snowballing. Finally, the two new concepts were judged as essential to cater for different individual judgements and to minimise the risk of excluding primary studies that ought to be included. © 2022 The Authors

Context: Exploiting software development related data from software-development intensive organizations to support tactical and strategic decision making is a challenge. Combining data-driven approaches with expert knowledge has been highlighted as a sensible approach for leading software-development intensive organizations to rightful decision-making improvements. However, most of the existing proposals lack of important aspects that hinders their industrial uptake such as: customization guidelines to fit the proposals to other contexts and/or automatic or semi-automatic data collection support for putting them forward in a real organization. As a result, existing proposals are rarely used in the industrial context. Objective: Support software-development intensive organizations with guidance and tools for exploiting software development related data and expert knowledge to improve their decision making. Method: We have developed a novel method called SESSI (Specification and Estimation of Software Strategic Indicators) that was articulated from industrial experiences with Nokia, Bittium, Softeam and iTTi in the context of Q-Rapids European project following a design science approach. As part of the industrial summative evaluation, we performed the first case study focused on the application of the method. Results: We detail the phases and steps of the SESSI method and illustrate its application in the development of ModelioNG, a software product of Modeliosoft development firm. Conclusion: The application of the SESSI method in the context of ModelioNG case study has provided us with useful feedback to improve the method and has evidenced that applying the method was feasible in this context. © 2020 Elsevier B.V.