Background: A novel framework in management science introduces collaboration-style focused personas (bee, ant, and leech) built on human values and their relationship to team performance. Studies on human values in software engineering until now have only focused on adapting human values to the engineering process. The persona framework leverages how values may affect performance in software engineering. Aims: We investigate the relationship of individual and team performance with computationally detected personas based on the content of different communication channels. Method: We conducted an empirical study with 61 students studying software quality management and correlated personas with their exam-based individual and project-based team performance. Bee, ant and leech personas were detected by an AI tool using intra-team WhatsApp and MSTeams textual communication. Results: We found a significant positive correlation of ant persona and a significant negative correlation of leech persona with team performance. In addition, the detection of computational personas differed significantly between communication channels. Conclusions: Informal meetups among team members and team-building training can promote antness among team members by improving a sense of security and belonging for a healthy competitive environment. Additionally, multiple distinct communication data channels are essential for forming reliable results when employing AI and other computational tools.

Background: Large language models (LLM) suffer from various forms of biases due to the biased datasets used to train the models. At the same time, human cognitive biases have an equal propensity to express themselves when using LLMs for software engineering tasks. Software testing is a critical phase of the software development life cycle. Confirmation bias is reported to have deteriorated software testing by designing more specification-consistent test cases compared to specificationinconsistent test cases. However, there is a lack of debiasing (mitigation) strategies in this regard. Aims: In this paper, first, we investigate whether the LLM model suffers from confirmation bias while performing software testing tasks. Second, we propose a vision of debasing confirmation bias in software testing via LLM. Method: We conducted an empirical study to detect confirmation bias by an LLM (ChatGPT4.0) in the design of functional test cases. Based on empirical findings, we used the analytical paradigm to design a multi-agent system. Results: We present a vision for debiasing confirmation bias in functional software testing by leveraging LLMs via a multi-agent approach. Conclusions: The proposed vision may improve the performance of LLMs in terms of reduced confirmation bias and serve as a debiasing technique for functional software testing.

Abstract The Agile Manifesto emphasizes individuals and interactions over processes and tools. However, after the COVID-19 pandemic, interaction in software development changed, and companies are trying to find new practices in the hybrid environment. Hybrid work research points to the benefits for the individual, whereas companies have begun to form new rules and policies to get employees back to the office. To find a balance benefiting all, companies need to find new ways to connect and communicate. This paper explores how hybrid work impacts the sense of belonging in agile software development. We conducted interviews (N = 38) and a workshop (N = 15) with professionals from three case organizations. Our thematic analysis identifies key factors influencing belonging at the individual, team, and organizational levels. Our findings underline that continuous, conscious, and visible actions are needed at all levels to foster a sense of belonging. As hybrid work reduces spontaneous and random encounters, maintaining a sense of belonging requires planned efforts to recreate the informal interactions that once happened naturally.

Background: Software testers manifest confirmation bias (the cognitive tendency) when they design relatively more specification consistent test cases than specification inconsistent test cases. Time pressure may influence confirmation bias of testers per the research in the psychology discipline.Objective: We examine the manifestation of confirmation bias of software testers while designing functional test cases, and the effect of time pressure on confirmation bias in the same context.Method: We executed one internal and two external experimental replications concerning the original experimentation in Oulu. We analyse individual replications and meta-analyse our family of experiments (the original and replications) for joint results on the phenomena. Results: Our findings indicate a significant manifestation of confirmation bias by software testers during the designing of functional test cases. Time pressure significantly promoted confirmation bias among testers per the joint results of the family. The different experimental sites affected the results; however, we did not detect any effects of site-specific variables.Conclusion: Software testers should develop an outside-of-the-box thinking attitude to counter the manifestation of confirmation bias. Time pressure can be manoeuvred by centring manual suites on the designing and consequently the execution of inconsistent test cases, while automated testing focuses on consistent ones.

Background:The existing literature in software engineering reports adverse effects of confirmation bias on software testing. Confirmation bias among software testers leads to confirmatory behavior, which is designing or executing relatively more specification consistent test cases (confirmatory behavior) than specification inconsistent test cases (disconfirmatory behavior).Objective:We aim to explore the antecedents to confirmatory and disconfirmatory behavior of software testers. Furthermore, we aim to understand why and how those antecedents lead to (dis)confirmatory behavior.Method:We follow grounded theory method for the analyses of the data collected through semi-structured interviews with twelve software testers.Results:We identified twenty antecedents to (dis)confirmatory behavior, and classified them in nine categories. Experience and Time are the two major categories. Experience is a disconfirmatory category, which also determines which behavior (confirmatory or disconfirmatory) occurs first among software testers, as an effect of other antecedents. Time Pressure is a confirmatory antecedent of the Time category. It also contributes to the confirmatory effects of antecedents of other categories.Conclusion:The disconfirmatory antecedents, especially that belong to the testing process, e.g., test suite reviews by project team members, may help circumvent the deleterious effects of confirmation bias in software testing. If a team’s resources permit, the designing and execution of a test suite could be divided among the test team members, as different perspectives of testers may help to detect more errors. The results of our study are based on a single context where dedicated testing teams focus on higher levels of testing. The study’s scope does not account for the testing performed by developers. Future work includes exploring other contexts to extend our results.

One source of software project challenges and failures is the systematic errors introduced by human cognitive biases. Although extensively explored in cognitive psychology, investigations concerning cognitive biases have only recently gained popularity in software engineering research. This paper therefore systematically maps, aggregates and synthesizes the literature on cognitive biases in software engineering to generate a comprehensive body of knowledge, understand state-of-the-art research and provide guidelines for future research and practise. Focusing on bias antecedents, effects and mitigation techniques, we identified 65 articles (published between 1990 and 2016), which investigate 37 cognitive biases. Despite strong and increasing interest, the results reveal a scarcity of research on mitigation techniques and poor theoretical foundations in understanding and interpreting cognitive biases. Although bias-related research has generated many new insights in the software engineering community, specific bias mitigation techniques are still needed for software professionals to overcome the deleterious effects of cognitive biases on their work.

Confirmation bias is a person’s tendency to look for evidence that strengthens his/her prior beliefs rather than refutes them. Manifestation of confirmation bias in software testing may have adverse effects on software quality. Psychology research suggests that time pressure could trigger confirmation bias. In the software industry, this phenomenon may deteriorate software quality. In this study, we investigate whether testers manifest confirmation bias and how it is affected by time pressure in functional software testing. We performed a controlled experiment with 42 graduate students to assess manifestation of confirmation bias in terms of the conformity of their designed test cases to the provided requirements specification. We employed a one factor with two treatments between-subjects experimental design. We observed, overall, participants designed significantly more confirmatory test cases as compared to disconfirmatory ones, which is in line with previous research. However, we did not observe time pressure as an antecedent to an increased rate of confirmatory testing behaviour. People tend to design confirmatory test cases regardless of time pressure. For practice, we find it necessary that testers develop self-awareness of confirmation bias and counter its potential adverse effects with a disconfirmatory attitude. We recommend further replications to investigate the effect of time pressure as a potential contributor to the manifestation of confirmation bias.

Background: Time-pressure is an inevitable reality of software industry that influences the performance of software engineers. It may result in adverse effects on software quality or distort the perception of performance on executed tasks to differ from actual performance. Objective: We aim to investigate the effect of time-pressure on perceived and actual performance of software testers in the context of functional software testing. Method: We performed two controlled experiments with 87 graduate students in two academic terms. We assessed actual performance in terms of coverage (i.e. percentage of test cases correctly identified) and perceived performance using NASA-TLX. We have an independent factorial design for our experimental study. Results: The results reveal a significant effect of time-pressure on actual performance. However, we could not observe a significant effect of time-pressure on the perceived performance of the participants for the task undertaken. We also observed a significant negative correlation between actual and perceived performance when controlled for time-pressure and experimental session factors. Conclusion: Time-pressure affects the actual performance in a testing task but the perception of accomplishment by the testers is sustained irrespective of time-pressure, indicating an over-estimation issue. Perception of performance should be adjusted to align with reality to account for the effect of time pressure. This will lead to better self estimates of performance.

Background: Most of the experiments in software engineering (SE) employ students as subjects. This raises concerns about the realism of the results acquired through students and adaptability of the results to software industry. Aim: We compare students and professionals to understand how well students represent professionals as experimental subjects in SE research. Method: The comparison was made in the context of two test-driven development experiments conducted with students in an academic setting and with professionals in a software organization. We measured the code quality of several tasks implemented by both subject groups and checked whether students and professionals perform similarly in terms of code quality metrics. Results: Except for minor differences, neither of the subject groups is better than the other. Professionals produce larger, yet less complex, methods when they use their traditional development approach, whereas both subject groups perform similarly when they apply a new approach for the first time. Conclusion: Given a carefully scoped experiment on a development approach that is new to both students and professionals, similar performances are observed. Further investigation is necessary to analyze the effects of subject demographics and level of experience on the results of SE experiments.