Jürgen Börstler

[Background] A well-formulated research problem is essential for achieving practical relevance in Software Engineering (SE), yet there is a lack of structured guidance in this early phase. [Aims] Our goal is to introduce and evaluate seven attributes identified in the SE literature as relevant for formulating research problems (practical problem, context, implications/impacts, practitioners, evidence, objective, and research questions) in terms of their perceived importance and completeness, and learn how they can be applied. [Method] We conducted a workshop with 42 senior SE researchers during the ISERN 2024 meeting. The seven attributes were presented using a Problem Vision board filled with a research example. Participants discussed attributes in groups, shared written feedback, and individually completed a survey assessing their importance, completeness, and suggestions for improvement. [Results] The findings confirm the importance of the seven attributes in the formulation of industry-oriented research problems. Qualitative feedback illustrated how they can be applied in practice and revealed suggestions to refine them, such as incorporating financial criteria (e.g., ROI) into implications/impacts and addressing feasibility and constraints under evidence. [Conclusion] The results reaffirm the importance of the seven attributes in supporting a reflective and context-aware problem formulation. Adapting their use to specific research contexts can help to improve the alignment between academic research and industry needs.

Context: The evidence for the prevalence of test smells at the unit testing level has relied on the accuracy of detection tools, which have seen intense research in the last two decades. The Eager Test smell, one of the most prevalent, is often identified using simplified detection rules that practitioners find inadequate. Objective: We aim to improve the rules for detecting the Eager Test smell. Method: We reviewed the literature on test smells to analyze the definitions and detection rules of the Eager Test smell. We proposed a novel, unambiguous definition of the test smell and a heuristic to address the limitations of the existing rules. We evaluated our heuristic against existing detection rules by manually applying it to 300 unit test cases in Java. Results: Our review identified 56 relevant studies. We found that inadequate interpretations of original definitions of the Eager Test smell led to imprecise detection rules, resulting in a high level of disagreement in detection outcomes. Also, our heuristic detected patterns of eager and non-eager tests that existing rules missed. Conclusion: Our heuristic captures the essence of the Eager Test smell more precisely; hence, it may address practitioners' concerns regarding the adequacy of existing detection rules.

Context: 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. Objective: We investigate practitioners' perceptions regarding the relative importance of quality attributes of test cases and test suites and the challenges they face in ensuring the perceived important quality attributes. Method: 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. Results: We collected 354 responses from practitioners with a wide range 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. 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. Conclusion: 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. The findings can serve as a guideline for academic researchers when looking for research directions on the topic. The findings can also be used to encourage companies to provide more support to practitioners to achieve high-quality test cases and test suites.

[Context] The lack of practical relevance in many Software Engineering (SE) research contributions is often rooted in oversimplified views of industrial practice, weak industry connections, and poorly defined research problems. Clear criteria for evaluating SE research problems can help align their value, feasibility, and applicability with industrial needs. [Goal] In this paper, we introduce the Lean Research Inception (LRI) framework, designed to support the formulation and assessment of practically relevant research problems in SE. We describe its initial evaluation strategy conducted in a workshop with a network of SE researchers experienced in industry-academia collaboration and report the evaluation of its three assessment criteria (valuable, feasible, and applicable) regarding their importance in assessing practical relevance. [Method] We applied LRI retroactively to a published research paper, engaging workshop participants in discussing and assessing the research problem by applying the proposed criteria using a semantic differential scale. Participants provided feedback on the criteria's importance and completeness, drawn from their own experiences in industry-academia collaboration. [Results] The findings reveal an overall agreement on the importance of the three criteria - valuable (83.3%), feasible (76.2%), and applicable (73.8%) - for aligning research problems with industrial needs. Qualitative feedback suggested adjustments in terminology with a clearer distinction between feasible and applicable, and refinements for valuable by more clearly considering business value, ROI, and originality. [Conclusion] While LRI constitutes ongoing research and requires further evaluation, our results strengthen our confidence that the three criteria applied using the semantic differential scale can already help the community assess the practical relevance of SE research problems.