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Context: To reduce manual effort of extracting test cases from natural-language requirements, many approaches based on Natural Language Processing (NLP) have been proposed in the literature. Given the large amount of approaches in this area, and since many practitioners are eager to utilize such techniques, it is important to synthesize and provide an overview of the state-of-the-art in this area. Objective: Our objective is to summarize the state-of-the-art in NLP-assisted software testing which could benefit practitioners to potentially utilize those NLP-based techniques. Moreover, this can benefit researchers in providing an overview of the research landscape. Method: To address the above need, we conducted a survey in the form of a systematic literature mapping (classification). After compiling an initial pool of 95 papers, we conducted a systematic voting, and our final pool included 67 technical papers. Results: This review paper provides an overview of the contribution types presented in the papers, types of NLP approaches used to assist software testing, types of required input requirements, and a review of tool support in this area. Some key results we have detected are: (1) only four of the 38 tools (11%) presented in the papers are available for download; (2) a larger ratio of the papers (30 of 67) provided a shallow exposure to the NLP aspects (almost no details). Conclusion: This paper would benefit both practitioners and researchers by serving as an “index” to the body of knowledge in this area. The results could help practitioners utilizing the existing NLP-based techniques; this in turn reduces the cost of test-case design and decreases the amount of human resources spent on test activities. After sharing this review with some of our industrial collaborators, initial insights show that this review can indeed be useful and beneficial to practitioners. © 2020 Elsevier B.V., open access

Context: Continuous integration (CI) is a practice that aims to continuously verify quality aspects of a software intensive system both for functional and non-functional requirements (NFRs). Functional requirements are the inputs of development and can be tested in isolation, utilising either manual or automated tests. In contrast, some NFRs are difficult to test without functionality, for NFRs are often aspects of functionality and express quality aspects. Lacking this testability attribute makes NFR testing complicated and, therefore, underrepresented in industrial practice. However, the emergence of CI has radically affected software development and created new avenues for software quality evaluation and quality information acquisition. Research has, consequently, been devoted to the utilisation of this additional information for more efficient and effective NFR verification. Objective: We aim to identify the state-of-the-art of utilising the CI environment for NFR testing, hereinafter referred to as CI-NFR testing. Method: Through rigorous selection, from an initial set of 747 papers, we identified 47 papers that describe how NFRs are tested in a CI environment. Evidence-based analysis, through coding, is performed on the identified papers in this SLR. Results: Firstly, ten CI approaches are described by the papers selected, each describing different tools and nine different NFRs where reported to be tested. Secondly, although possible, CI-NFR testing is associated with eight challenges that adversely affect its adoption. Thirdly, the identified CI-NFR testing processes are tool-driven, but there is a lack of NFR testing tools that can be used in the CI environment. Finally, we proposed a CI framework for NFRs testing. Conclusion: A synthesised CI framework is proposed for testing various NFRs, and associated CI tools are also mapped. This contribution is valuable as results of the study also show that CI-NFR testing can help improve the quality of NFR testing i

Context: GUI-based testing is a sub-field of software testing research that has emerged in the last three decades. GUI-based testing techniques focus on verifying the functional conformance of the system under test (SUT) through its graphical user interface. However, despite the research domains growth, studies in the field have low reproducibility and comparability. One observed cause of these phenomena is identified as a lack of research rigor and commonly used metrics, including coverage metrics. Objective: We aim to identify the most commonly used metrics in the field and formulate a taxonomy of coverage metrics for GUI-based testing research. Method: We adopt an evidence-based approach to build the taxonomy through a systematic literature review of studies in the GUI-based testing domain. Identified papers are then analyzed with Open and Axial Coding techniques to identify hierarchical and mutually exclusive categories of metrics with common characteristics, usages, and applications. Results: Through the analysis of 169 papers and 315 metric definitions, we obtained a taxonomy with 55 codes (common names for metrics), 17 metric categories, and 4 higher level categories: Functional Level, GUI Level, Model Level and Code Level. We measure a higher number of mentions of Model and Code level metrics over Functional and GUI level metrics. Conclusions: We propose a taxonomy for use in future GUI-based testing research to improve the general quality of studies in the domain. In addition, the taxonomy is perceived to help enable more replication studies as well as macro-analysis of the current body of research. © 2022 Elsevier B.V.

In parallel to academic (peer-reviewed) literature (e.g., journal and conference papers), an enormous extent of grey literature (GL) has accumulated since the inception of software engineering (SE). GL is often defined as “literature that is not formally published in sources such as books or journal articles”, e.g., in the form of trade magazines, online blog-posts, technical reports, and online videos such as tutorial and presentation videos. GL is typically produced by SE practitioners. We have observed that researchers are increasingly using and benefitting from the knowledge available within GL. Related to the notion of GL is the notion of Multivocal Literature Reviews (MLRs) in SE, i.e., a MLR is a form of a Systematic Literature Review (SLR) which includes knowledge and/or evidence from the GL in addition to the peer-reviewed literature. MLRs are useful for both researchers and practitioners because they provide summaries of both the state-of-the-art and -practice in a given area. MLRs are popular in other fields and have started to appear in SE community. It is timely then for a Special Issue (SI) focusing on GL and MLRs in SE. From the pool of 13 submitted papers, and after following a rigorous peer review process, seven papers were accepted for this SI. In this introduction we provide a brief overview of GL and MLRs in SE, and then a brief summary of the seven papers published in this SI. © 2021

The relevance of context is particularly stressed in case studies, where it is said that “case study is an empirical method aimed at investigating contemporary phenomena in their context”. In this research, we classify context information and provide a context checklist for industrial software engineering. The checklist serves the purpose of (a) supporting researchers and practitioners in characterizing the context in which they are working; (b) supporting researchers with a checklist to identify relevant contextual information to elicit and report during primary and secondary studies. We utilized a systematic approach for constructing the classification of context information and provided a detailed definition for each item. We collected feedback from researchers as well as practitioners. The usefulness of the checklist was perceived more positively by researchers than practitioners, though they highlighted benefits (raising awareness of the importance of context and usefulness for management). The understandability was perceived positively by both practitioners and researchers. The checklist may serve as a “meta-model”, forming the basis for specific adaptations for different research areas, and as input for researchers deciding which context information to extract in systematic reviews. The checklist may also help researchers in reporting context in research papers. © 2021

Context: Continuous experimentation and A/B testing is an established industry practice that has been researched for more than 10 years. Our aim is to synthesize the conducted research. Objective: We wanted to find the core constituents of a framework for continuous experimentation and the solutions that are applied within the field. Finally, we were interested in the challenges and benefits reported of continuous experimentation. Methods: We applied forward snowballing on a known set of papers and identified a total of 128 relevant papers. Based on this set of papers we performed two qualitative narrative syntheses and a thematic synthesis to answer the research questions. Results: The framework constituents for continuous experimentation include experimentation processes as well as supportive technical and organizational infrastructure. The solutions found in the literature were synthesized to nine themes, e.g. experiment design, automated experiments, or metric specification. Concerning the challenges of continuous experimentation, the analysis identified cultural, organizational, business, technical, statistical, ethical, and domain-specific challenges. Further, the study concludes that the benefits of experimentation are mostly implicit in the studies. Conclusion: The research on continuous experimentation has yielded a large body of knowledge on experimentation. The synthesis of published research presented within include recommended infrastructure and experimentation process models, guidelines to mitigate the identified challenges, and what problems the various published solutions solve. © 2021 The Authors, open access

Context: Continuous experimentation and A/B testing is an established industry practice that has been researched for more than 10 years. Our aim is to synthesize the conducted research. Objective: We wanted to find the core constituents of a framework for continuous experimentation and the solutions that are applied within the field. Finally, we were interested in the challenges and benefits reported of continuous experimentation. Methods: We applied forward snowballing on a known set of papers and identified a total of 128 relevant papers. Based on this set of papers we performed two qualitative narrative syntheses and a thematic synthesis to answer the research questions. Results: The framework constituents for continuous experimentation include experimentation processes as well as supportive technical and organizational infrastructure. The solutions found in the literature were synthesized to nine themes, e.g. experiment design, automated experiments, or metric specification. Concerning the challenges of continuous experimentation, the analysis identified cultural, organizational, business, technical, statistical, ethical, and domain-specific challenges. Further, the study concludes that the benefits of experimentation are mostly implicit in the studies. Conclusion: The research on continuous experimentation has yielded a large body of knowledge on experimentation. The synthesis of published research presented within include recommended infrastructure and experimentation process models, guidelines to mitigate the identified challenges, and what problems the various published solutions solve. © 2021 The Authors, open access

Context: Continuous experimentation and A/B testing is an established industry practice that has been researched for more than 10 years. Our aim is to synthesize the conducted research. Objective: We wanted to find the core constituents of a framework for continuous experimentation and the solutions that are applied within the field. Finally, we were interested in the challenges and benefits reported of continuous experimentation. Methods: We applied forward snowballing on a known set of papers and identified a total of 128 relevant papers. Based on this set of papers we performed two qualitative narrative syntheses and a thematic synthesis to answer the research questions. Results: The framework constituents for continuous experimentation include experimentation processes as well as supportive technical and organizational infrastructure. The solutions found in the literature were synthesized to nine themes, e.g. experiment design, automated experiments, or metric specification. Concerning the challenges of continuous experimentation, the analysis identified cultural, organizational, business, technical, statistical, ethical, and domain-specific challenges. Further, the study concludes that the benefits of experimentation are mostly implicit in the studies. Conclusion: The research on continuous experimentation has yielded a large body of knowledge on experimentation. The synthesis of published research presented within include recommended infrastructure and experimentation process models, guidelines to mitigate the identified challenges, and what problems the various published solutions solve. © 2021 The Authors, open access

Context: Continuous experimentation and A/B testing is an established industry practice that has been researched for more than 10 years. Our aim is to synthesize the conducted research. Objective: We wanted to find the core constituents of a framework for continuous experimentation and the solutions that are applied within the field. Finally, we were interested in the challenges and benefits reported of continuous experimentation. Methods: We applied forward snowballing on a known set of papers and identified a total of 128 relevant papers. Based on this set of papers we performed two qualitative narrative syntheses and a thematic synthesis to answer the research questions. Results: The framework constituents for continuous experimentation include experimentation processes as well as supportive technical and organizational infrastructure. The solutions found in the literature were synthesized to nine themes, e.g. experiment design, automated experiments, or metric specification. Concerning the challenges of continuous experimentation, the analysis identified cultural, organizational, business, technical, statistical, ethical, and domain-specific challenges. Further, the study concludes that the benefits of experimentation are mostly implicit in the studies. Conclusion: The research on continuous experimentation has yielded a large body of knowledge on experimentation. The synthesis of published research presented within include recommended infrastructure and experimentation process models, guidelines to mitigate the identified challenges, and what problems the various published solutions solve. © 2021 The Authors, open access

Context: Continuous experimentation and A/B testing is an established industry practice that has been researched for more than 10 years. Our aim is to synthesize the conducted research. Objective: We wanted to find the core constituents of a framework for continuous experimentation and the solutions that are applied within the field. Finally, we were interested in the challenges and benefits reported of continuous experimentation. Methods: We applied forward snowballing on a known set of papers and identified a total of 128 relevant papers. Based on this set of papers we performed two qualitative narrative syntheses and a thematic synthesis to answer the research questions. Results: The framework constituents for continuous experimentation include experimentation processes as well as supportive technical and organizational infrastructure. The solutions found in the literature were synthesized to nine themes, e.g. experiment design, automated experiments, or metric specification. Concerning the challenges of continuous experimentation, the analysis identified cultural, organizational, business, technical, statistical, ethical, and domain-specific challenges. Further, the study concludes that the benefits of experimentation are mostly implicit in the studies. Conclusion: The research on continuous experimentation has yielded a large body of knowledge on experimentation. The synthesis of published research presented within include recommended infrastructure and experimentation process models, guidelines to mitigate the identified challenges, and what problems the various published solutions solve. © 2021 The Authors, open access