Context: Regression testing is a well-researched area. However, the majority regression testing techniques proposed by the researchers are not getting the attention of the practitioners. Communication gaps between industry and academia and disparity in the regression testing goals are the main reasons. Close collaboration can help in bridging the communication gaps and resolving the disparities.Objective: The study aims at exploring the views of academics and practitioners about the goals of regression testing. The purpose is to investigate the commonalities and differences in their viewpoints and defining some common goals for the success of regression testing.Method: We conducted a focus group study, with 7 testing experts from industry and academia. 4 testing practitioners from 2companies and 3 researchers from 2 universities participated in the study. We followed GQM approach, to elicit the regression testing goals, information needs, and measures.Results: 43 regression testing goals were identified by the participants, which were reduced to 10 on the basis of similarity among the identified goals. Later during the priority assignment process, 5 goals were discarded, because the priority assigned to these goals was very low. Participants identified 47 information needs/questions required to evaluate the success of regression testing with reference to goal G5 (confidence). Which were then reduced to10 on the basis of similarity. Finally, we identified measures to gauge those information needs/questions, which were corresponding to the goal (G5).Conclusions: We observed that participation level of practitioners and researchers during the elicitation of goals and questions was same. We found a certain level of agreement between the participants regarding the regression testing definitions and goals.But there was some level of disagreement regarding the priorities of the goals. We also identified the need to implement a regression testing evaluation framework in the participating companies.

Regression testing is challenging because of its complexity and the amount of effort and time it requires, especially in large-scale environments with continuous integration and delivery. Regression test selection and prioritization techniques have been proposed in the literature to address the regression testing challenges, but adoption rates of these techniques in industry are not encouraging. One of the possible reasons could be the disparity in the regression testing goals in industry and literature. 

This work compares the research perspective to industry practice on regression testing goals, corresponding information needs, and metrics required to evaluate these goals. We have conducted a literature review of 44 research papers and a survey with 56 testing practitioners. The survey comprises 11 interviews and 45 responses to an online questionnaire. 

We identified that industry and research accentuate different regression testing goals. For instance, the literature emphasizes increasing the fault detection rates of test suites and early identification of critical faults. In contrast, the practitioners' focus is on test suite maintenance, controlled fault slippage, and awareness of changes. Similarly, the literature suggests maintaining information needs from test case execution histories to evaluate regression testing techniques based on various metrics, whereas, at large, the practitioners do not use the metrics suggested in the literature. 

To bridge the research and practice gap, based on the literature and survey findings, we have created a goal-question-metric (GQM) model that maps the regression testing goals, associated information needs, and metrics from both perspectives. The GQM model can guide researchers in proposing new techniques closer to industry contexts. Practitioners can benefit from information needs and metrics presented in the literature and can use GQM as a tool to follow their regression testing goals. 

Context: A majority of the regression testing techniques proposed by the research have not been adopted in industry. To increase adoption rates, we need to better understand the practitioners' perspectives on regression testing.

Objective: This study aims at exploring the regression testing state of practice in the large-scale embedded software development. The study has two objectives, 1) to highlight the potential challenges in practice, and 2) to identify the industry-relevant research areas regarding regression testing.

Method: We conducted a qualitative study in two large-scale embedded software development companies, where we carried out semi-structured interviews with representatives from five software testing teams. We did conduct the detailed review of the process documentation of the companies to complement/validate the findings of the interviews.

Results: Mostly, the practitioners run regression testing with a selected scope, the selection of scope depends upon the size, complexity, and location of the change. Test cases are prioritized on the basis of risk and critical functionality. The practitioners rely on their knowledge and experience for the decision making regarding selection and prioritization of test cases.The companies are using both automated and manual regression testing, and mainly they rely on in-house developed tools for test automation. The challenges identified in the companies are: time to test, information management, test suite maintenance, lack of communication, test selection/prioritization, lack of assessment, etc. The proposed improvements are in line with the identified challenges. Regression testing goals identified in this study are customer satisfaction, critical defect detection, confidence, effectiveness, efficiency, and controlled slip through of faults.

Conclusions: Considering the current state of practice and identified challenges we conclude that there is a need to reconsider the regression test strategy in the companies. Researchers need to analyze the industry perspective while proposing new regression testing techniques. The industry-academia collaboration projects would be a good platform in this regard.

Regression testing is a means to assure that a change in the software, or

its execution environment, does not introduce new defects. It involves the expensive

undertaking of rerunning test cases. Several techniques have been proposed

to reduce the number of test cases to execute in regression testing, however, there

is no research on how to assess industrial relevance and applicability of such techniques.

We conducted a systematic literature review with the following two goals:

rstly, to enable researchers to design and present regression testing research with

a focus on industrial relevance and applicability and secondly, to facilitate the industrial

adoption of such research by addressing the attributes of concern from the

practitioners' perspective. Using a reference-based search approach, we identied

1068 papers on regression testing. We then reduced the scope to only include papers

with explicit discussions about relevance and applicability (i.e. mainly studies

involving industrial stakeholders). Uniquely in this literature review, practitioners

were consulted at several steps to increase the likelihood of achieving our aim of

identifying factors important for relevance and applicability. We have summarised

the results of these consultations and an analysis of the literature in three taxonomies,

which capture aspects of industrial-relevance regarding the regression

testing techniques. Based on these taxonomies, we mapped 38 papers reporting

the evaluation of 26 regression testing techniques in industrial settings.

Replication studies help solidify and extend knowledge by evaluating previous studies’ findings. Software engineering literature showed that too few replications are conducted focusing on software artifacts without the involvement of humans. This study aims to replicate an artifact-based study on software testing to address the gap related to replications. In this investigation, we focus on (i) providing a step-by-step guide of the replication, reflecting on challenges when replicating artifact-based testing research and (ii) evaluating the replicated study concerning the validity and robustness of the findings. We replicate a test case prioritization technique proposed by Kwon et al. We replicated the original study using six software programs, four from the original study and two additional software programs. We automated the steps of the original study using a Jupyter notebook to support future replications. Various general factors facilitating replications are identified, such as (1) the importance of documentation; (2) the need for assistance from the original authors; (3) issues in the maintenance of open-source repositories (e.g., concerning needed software dependencies, versioning); and (4) availability of scripts. We also noted observations specific to the study and its context, such as insights from using different mutation tools and strategies for mutant generation. We conclude that the study by Kwon et al. is partially replicable for small software programs and could be automated to facilitate software practitioners, given the availability of required information. However, it is hard to implement the technique for large software programs with the current guidelines. Based on lessons learned, we suggest that the authors of original studies need to publish their data and experimental setup to support the external replications. © 2023, The Author(s).

Context: Practitioners working in large-scale software development face many challenges in regression testing activities. One of the reasons is the lack of a structured regression testing process. In this regard, checklists can help practitioners keep track of essential regression testing activities and add structure to the regression testing process to a certain extent. Objective: This study aims to introduce regression testing checklists so test managers/teams can use them: (1) to assess whether test teams/members are ready to begin regression testing, and (2) to keep track of essential regression testing activities while planning and executing regression tests. Method: We used interviews, workshops, and questionnaires to design, evolve, and evaluate regression testing checklists. In total, 25 practitioners from 12 companies participated in creating the checklist. Twenty-three of them participated in checklists evolution and evaluation. Results: We identified activities practitioners consider significant while planning, performing, and analyzing regression testing. We designed regression testing checklists based on these activities to help practitioners make informed decisions during regression testing. With the help of practitioners, we evolved these checklists into two iterations. Finally, the practitioners provided feedback on the proposed checklists. All respondents think the proposed checklists are useful and customizable for their environments, and 80% think checklists cover aspects essential for regression testing. Conclusion: The proposed regression testing checklists can be useful for test managers to assess their team/team members’ readiness and decide when to start and stop regression testing. The checklists can be used to record the steps required while planning and executing regression testing. Further, these checklists can provide a basis for structuring the regression testing process in varying contexts. © 2023 The Author(s)