Expert recommendation with usage expertise

Ma, D.C.; Schüler, David; Zimmermann, Thomas; Sillito, Jonathan

doi:10.1109/icsm.2009.5306386

Cited by 51 publications

(20 citation statements)

References 14 publications

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“…Nguyen et al [2] and Ma et al [3] proposed approaches to fix the recommendation issues by analyzing the implementation history and expertise of developers. Thongtanunam et al [4] and Yu et al [5] focused on the automatic pull-request reviewer recommendation problem by analyzing the development history and social connections of individuals in the software community.…”

Section: A Introductionmentioning

confidence: 99%

Who Will be Interested in? A Contributor Recommendation Approach for Open Source Projects

Zhang

Wang

Yin

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-The crowds' continuous participation and contribution are the key factors for the success of open source projects. However, among the massive competitors, it is difficult for a project to attract enough contributors by just passively waiting for enthusiasts to join in. Instead, it should actively seek gifted developers. Most of the current studies mainly focus on recommending experts inside a repository for some specific development tasks. In this paper, we propose a novel approach A. IntroductionOpen source software (OSS) has become increasingly popular in software development. Quite different from the traditional software development, OSS is driven by massive crowds including developers, users, managers and so on. These stakeholders involve in OSS by interests, and most of them have their own full-time job and can only spend spare time on OSS. They can join in or withdraw from it at any time. Nevertheless, OSS has achieved great success at creating highquality software like Linux, MySQL, Spark and so on, and is viewed as "eating the software world" by "the Future of Open Source Survey" [1].The behavior of open source itself does not necessarily result in a project's success, but the continuous participation and active contribution from the crowds do play a crucial role. In GitHub, there are more than 48 million open source projects. However, over 2 million projects are not forked or watched by anyone after releasing, and 15.1% of them were not updated for more than a year and finally failed. Even DOI reference number: 10.18293/SEKE2017-067 for those projects which used to be successful will languish without continuous contribution of developers.Therefore, finding and attracting the right developers to participate is quite crucial for OSS. On the one hand, proper developers can provide necessary technical expertise that a project needs; On the other hand, suitable projects can inspire developers to participate in and to contribute their innovations continuously. However, there is a massive amount of competitive OSS, and developers are often limited by their time and energy to browse and choose from all the related projects. An automatic approach to bridge the gap between developers and projects and match them means much for both developers and projects.Several studies have explored the act of recommending developers to open source projects. Nguyen et al. [2] and Ma et al. [3] proposed approaches to fix the recommendation issues by analyzing the implementation history and expertise of developers. Thongtanunam et al. [4] and Yu et al. [5] focused on the automatic pull-request reviewer recommendation problem by analyzing the development history and social connections of individuals in the software community. Most of these studies mainly focus on specific software tasks and limit the recommended candidates to the core developers of the projects. By contrast, the current study is rooted in the granularity of open source repositories, and our output allows recommendations of external experts throughout the...

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Section: A Introductionmentioning

confidence: 99%

Who Will be Interested in? A Contributor Recommendation Approach for Open Source Projects

Zhang

Wang

Yin

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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“…Prominent applications for these techniques are online marketing [26], web search [27], social media content filtering [28], and entertainment services [29], [30], [31]. Another line of research is dedicated to recommending experts for various applications in knowledge-heavy contexts, such as academic research and software development [32], [33]. In particular, recommender systems are proposed in a variety of software engineering related scopes and are targeted towards improving the efficiency of development and quality assurance activities.…”

Section: Recommender Systemsmentioning

confidence: 99%

Does Reviewer Recommendation Help Developers?

Kovalenko

Tintarev

Pasynkov³

et al. 2020

IIEEE Trans. Software Eng.

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Abstract-Selecting reviewers for code changes is a critical step for an efficient code review process. Recent studies propose automated reviewer recommendation algorithms to support developers in this task. However, the evaluation of recommendation algorithms, when done apart from their target systems and users (i.e., code review tools and change authors), leaves out important aspects: perception of recommendations, influence of recommendations on human choices, and their effect on user experience. This study is the first to evaluate a reviewer recommender in vivo. We compare historical reviewers and recommendations for over 21,000 code reviews performed with a deployed recommender in a company environment and set out to measure the influence of recommendations on users' choices, along with other performance metrics. Having found no evidence of influence, we turn to the users of the recommender. Through interviews and a survey we find that, though perceived as relevant, reviewer recommendations rarely provide additional value for the respondents. We confirm this finding with a larger study at another company. The confirmation of this finding brings up a case for more user-centric approaches to designing and evaluating the recommenders. Finally, we investigate information needs of developers during reviewer selection and discuss promising directions for the next generation of reviewer recommendation tools. Preprint: https://doi.org/10.5281/zenodo.1404814

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“…Bird et al [25] analyzed the communication and co-ordination activities of the participants by mining email archives. Ma et al [67] proposed a technique that uses implementation expertise (i.e., developers usage of API methods) to identify developers. Weissgerber et al [102] depicts the relationship between the lifetime of the project and the number of files each author updates by analyzing and visualizing the check-in information for open source projects.…”

Section: Developer Recommendationmentioning

confidence: 99%

Effective assignment and assistance to software developers and reviewers

Zanjani

2016

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

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DEDICATIONTo my parents and my sister. In this dissertation, we first present the automated solutions for Software Change Impact Analysis based on interaction and code review activities of developers. Then we provide explanation for two separate developer expertise models which use the micro-levels of human-to-code and human-to-human interactions from the previous code review and interaction activities of developers. Next, we present a reviewer expertise model based on code review activities of developers and show how this expertise model can be used for Code Reviewer Recommendation. At the end we examine the influential features that characterize the acceptance probability of a submitted patch (implemented code change) by developers. We present a predictive model that classifies whether a patch will be accepted or not as soon as it is submitted for code review in order to assist developers and reviewers in prioritizing and focusing their efforts.A rigorous empirical validation on large open source and commercial systems shows that the solutions based on the presented methodology outperform several existing solutions. The quantitative gains of our solutions across a spectrum of evaluation metrics along with their statistical significance are reported.vi

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Expert recommendation with usage expertise

Cited by 51 publications

References 14 publications

Who Will be Interested in? A Contributor Recommendation Approach for Open Source Projects

Who Will be Interested in? A Contributor Recommendation Approach for Open Source Projects

Does Reviewer Recommendation Help Developers?

Effective assignment and assistance to software developers and reviewers

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