Mutation testing exploits artificial faults to measure the adequacy of test suites and guide their improvement. It has become an extremely popular testing technique as evidenced by the vast literature, numerous tools, and research events on the topic. Previous survey papers have successfully compiled the state of research, its evolution, problems, and challenges. However, the use of mutation testing in practice is still largely unexplored. In this paper, we report the results of a thorough study on the use of mutation testing in GitHub projects. Specifically, we first performed a search for mutation testing tools, 127 in total, and we automatically searched the GitHub repositories including evidence of their use. Then, we focused on the top ten most widely used tools, based on the previous results, and manually revised and classified over 3.5K GitHub active repositories importing them. Among other findings, we observed a recent upturn in interest and activity, with Infection (PHP), PIT (Java) and Humbug (PHP) being the most widely used mutation tools in recent years. The predominant use of mutation testing is development, followed by teaching and learning, and research projects, although with significant differences among mutation tools found in the literature—less adopted and largely used in teaching and research—and those found in GitHub only—more popular and more widely used in development. Our work provides a new and encouraging perspective on the state of practice of mutation testing.
Fri 19 MayDisplayed time zone: Hobart change
13:45 - 15:15 | Fault injection and mutationJournal-First Papers / NIER - New Ideas and Emerging Results / SEIP - Software Engineering in Practice / DEMO - Demonstrations / Technical Track at Meeting Room 105 Chair(s): Lingxiao Jiang Singapore Management University | ||
13:45 15mTalk | Coverage Guided Fault Injection for Cloud Systems Technical Track Yu Gao Institute of Software, Chinese Academy of Sciences, China, Wensheng Dou Institute of Software Chinese Academy of Sciences, Dong Wang Institute of software, Chinese academy of sciences, Wenhan Feng Institute of Software Chinese Academy of Sciences, Jun Wei Institute of Software at Chinese Academy of Sciences; University of Chinese Academy of Sciences; University of Chinese Academy of Sciences Chongqing School, Hua Zhong Institute of Software Chinese Academy of Sciences, Tao Huang Institute of Software Chinese Academy of Sciences Pre-print | ||
14:00 15mTalk | Diver: Oracle-Guided SMT Solver Testing with Unrestricted Random Mutations Technical Track | ||
14:15 15mTalk | Identifying Defect Injection Risks from Analysis and Design Diagrams: An Industrial Case Study at Sony SEIP - Software Engineering in Practice Yoji Imanishi Sony Global Manufacturing&Operations, Kazuhiro Kumon Sony Global Manufacturing&Operations, Shuji Morisaki Nagoya University | ||
14:30 7mTalk | DaMAT: A Data-driven Mutation Analysis Tool DEMO - Demonstrations Enrico Viganò University of Luxembourg, Oscar Cornejo SnT Centre, University of Luxembourg, Fabrizio Pastore University of Luxembourg, Lionel Briand University of Luxembourg; University of Ottawa Pre-print | ||
14:37 7mTalk | Mutation testing in the wild: findings from GitHub Journal-First Papers Ana B. Sánchez University of Seville, Pedro Delgado-Pérez Universidad de Cádiz, Inmaculada Medina-Bulo Universidad de Cádiz, Sergio Segura University of Seville Link to publication DOI | ||
14:45 7mTalk | An Experimental Assessment of Using Theoretical Defect Predictors to Guide Search-Based Software Testing Journal-First Papers Anjana Perera Oracle Labs, Australia, Aldeida Aleti Monash University, Burak Turhan University of Oulu, Marcel Böhme MPI-SP, Germany and Monash University, Australia Link to publication DOI | ||
14:52 7mTalk | Assurance Cases as Data: A Manifesto NIER - New Ideas and Emerging Results Claudio Menghi McMaster University, Canada, Torin Viger , Alessio Di Sandro University of Toronto, Chris Rees Critical Systems Labs, Jeffrey Joyce Critical System Labs Inc., Marsha Chechik University of Toronto | ||
15:00 7mTalk | Predictive Mutation Analysis via Natural Language Channel in Source Code Journal-First Papers Jinhan Kim KAIST, Juyoung Jeon Handong Global University, Shin Hong Handong Global University, Shin Yoo KAIST Link to publication Pre-print |