Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009 and 2022, covering 6,117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions, including conducting further empirical validation and industrial studies on ML, reconsidering deficient SE methods, documenting and automating data collection and pipeline processes, reexamining how industrial practitioners distribute their proprietary data, and implementing incremental ML approaches.

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Existing work on the practical impact of software engineering (SE) research examines industrial relevance rather than adoption of study results, hence the question of how results have been practically applied remains open. To answer this and investigate the outcomes of impactful research, we performed a quantitative and qualitative analysis of 4,354 SE patents citing 1,690 SE papers published in four leading SE venues between 1975–2017. Moreover, we conducted a survey on 475 authors of 593 top-cited and awarded publications, achieving 26% response rate. Overall, researchers have equipped practitioners with various tools, processes, and methods, and improved many existing products. SE practice values knowledge-seeking research and is impacted by diverse cross-disciplinary SE areas. Practitioner-oriented publication venues appear more impactful than researcher-oriented ones, while industry-related tracks in conferences could enhance their impact. Some research works did not reach a wide footprint due to limited funding resources or unfavorable cost-benefit trade-off of the proposed solutions. The need for higher SE research funding could be corroborated through a dedicated empirical study. In general, the assessment of impact is subject to its definition. Therefore, academia and industry could jointly agree on a formal description to set a common ground for subsequent research on the topic.@en

In order to understand the state and evolution of the entirety of open source software we need to get a handle on the set of distinct software projects. Most of open source projects presently utilize Git, which is a distributed version control system allowing easy creation of clones and resulting in numerous repositories that are almost entirely based on some parent repository from which they were cloned. Git commits are unlikely to get produce and represent a way to group cloned repositories. We use World of Code infrastructure containing approximately 2B commits and 100M repositories to create and share such a map. We discover that the largest group contains almost 14M repositories most of which are unrelated to each other. As it turns out, the developers can push git object to an arbitrary repository or pull objects from unrelated repositories, thus linking unrelated repositories. To address this, we apply Louvain community detection algorithm to this very large graph consisting of links between commits and projects. The approach successfully reduces the size of the megacluster with the largest group of highly interconnected projects containing under 400K repositories. We expect that the resulting map of related projects as well as tools and methods to handle the very large graph will serve as a reference set for mining software projects and other applications. Further work is needed to determine different types of relationships among projects induced by shared commits and other relationships, for example, by shared source code or similar filenames.

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We present a dataset of open source software developed mainly by enterprises rather than volunteers. This can be used to address known generalizability concerns, and, also, to perform research on open source business software development. Based on the premise that an enterprise's employees are likely to contribute to a project developed by their organization using the email account provided by it, we mine domain names associated with enterprises from open data sources as well as through white-and blacklisting, and use them through three heuristics to identify 17 264 enterprise GitHub projects. We provide these as a dataset detailing their provenance and properties. A manual evaluation of a dataset sample shows an identification accuracy of 89%. Through an exploratory data analysis we found that projects are staffed by a plurality of enterprise insiders, who appear to be pulling more than their weight, and that in a small percentage of relatively large projects development happens exclusively through enterprise insiders.

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GitHub projects can be easily replicated through the site's fork process or through a Git clone-push sequence. This is a problem for empirical software engineering, because it can lead to skewed results or mistrained machine learning models. We provide a dataset of 10.6 million GitHub projects that are copies of others, and link each record with the project's ultimate parent. The ultimate parents were derived from a ranking along six metrics. The related projects were calculated as the connected components of an 18.2 million node and 12 million edge denoised graph created by directing edges to ultimate parents. The graph was created by filtering out more than 30 hand-picked and 2.3 million pattern-matched clumping projects. Projects that introduced unwanted clumping were identified by repeatedly visualizing shortest path distances between unrelated important projects. Our dataset identified 30 thousand duplicate projects in an existing popular reference dataset of 1.8 million projects. An evaluation of our dataset against another created independently with different methods found a significant overlap, but also differences attributed to the operational definition of what projects are considered as related.

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The establishment of the Mining Software Repositories (MSR) data showcase conference track has encouraged researchers to provide data sets as a basis for further empirical studies. The objective of this study is to examine the usage of data papers published in the MSR proceedings in terms of use frequency, users, and use purpose. Data track papers were collected from the MSR data showcase track and through the manual inspection of older MSR proceedings. The use of data papers was established through manual citation searching followed by reading the citing studies and dividing them into strong and weak citations. Contrary to weak, strong citations truly use the data set of a data paper. Data papers were then manually clustered based on their content, whereas their strong citations were classified by hand according to the knowledge areas of the Guide to the Software Engineering Body of Knowledge. A survey study on 108 authors and users of data papers provided further insights regarding motivation and effort in data paper production, encouraging and discouraging factors in data set use, and future desired direction regarding data papers. We found that 65% of the data papers have been used in other studies, with a long-tail distribution in the number of strong citations. Weak citations to data papers usually refer to them as an example. MSR data papers are cited in total less than other MSR papers. A considerable number of the strong citations stem from the teams that authored the data papers. Publications providing Version Control System (VCS) primary and derived data are the most frequent data papers and the most often strongly cited ones. Enhanced developer data papers are the least common ones, and the second least frequently strongly cited. Data paper authors tend to gather data in the context of other research. Users of data sets appreciate high data quality and are discouraged by lack of replicability of data set construction. Data related to machine learning or derived from the manufacturing sector are two suggestions of the respondents for future data papers. Overall, data papers have provided the foundation for a significant number of studies, but there is room for improvement in their utilization. This can be done by setting a higher bar for their publication, by encouraging their use, by promoting open science initiatives, and by providing incentives for the enrichment of existing data collections.

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Introduction: The establishment of the Mining Software Repositories (MSR) Data Showcase conference track has encouraged researchers to provide more data sets as a basis for further empirical studies. Objectives: Examine the usage of the data papers published in the MSR proceedings in terms of use frequency, users, and use purpose. Methods: Data track papers were collected from the MSR Data Showcase and through the manual inspection of older MSR proceedings. The use of data papers was established through citation searching followed by reading the studies that have cited them. Data papers were then clustered based on their content, whereas their citations were classified according to the knowledge areas of the Guide to the Software Engineering Body of Knowledge. Results: We found that 65% of the data papers have been used in other studies, with a long-tail distribution in the number of citations. MSR data papers are cited less than other MSR papers. A considerable number of the citations stem from the teams that authored the data papers. Publications providing repository data and metadata are the most frequent data papers and the most often cited ones. Mobile application data papers are the least common ones, but the second most frequently cited. Conclusion: Data papers have provided the foundation for a significant number of studies, but there is room for improvement in their utilization. This can be done by setting a higher bar for their publication, by encouraging their use, and by providing incentives for the enrichment of existing data collections.

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