The impact of test case summaries on bug fixing performance
Open Access
- 14 May 2016
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 547-558
Abstract
Automated test generation tools have been widely investigated with the goal of reducing the cost of testing activities. However, generated tests have been shown not to help developers in detecting and finding more bugs even though they reach higher structural coverage compared to manual testing. The main reason is that generated tests are diff-cult to understand and maintain. Our paper proposes an approach, coined TestDescriber, which automatically generates test case summaries of the portion of code exercised by each individual test, thereby improving understandability. We argue that this approach can complement the current techniques around automated unit test generation or searchbased techniques designed to generate a possibly minimal set of test cases. In evaluating our approach we found that (1) developers find twice as many bugs, and (2) test case summaries significantly improve the comprehensibility of test cases, which is considered particularly useful by developers.</pKeywords
This publication has 53 references indexed in Scilit:
- 1600 faults in 100 projects: automatically finding faults while achieving high coverage with EvoSuiteEmpirical Software Engineering, 2013
- Labeling source code with information retrieval methods: an empirical studyEmpirical Software Engineering, 2013
- Parameter tuning or default values? An empirical investigation in search-based software engineeringEmpirical Software Engineering, 2013
- Students as surrogates for practicing accountants: Further evidenceAccounting Forum, 2012
- On the Interplay Between Software Testing and Evolution and its Effect on Program Comprehension2008
- Automatic summarising: The state of the artInformation Processing & Management, 2007
- Automatic Testing of Object-Oriented Software2007
- JCrasher: an automatic robustness tester for JavaSoftware: Practice and Experience, 2004
- A Critique and Improvement of the CL Common Language Effect Size Statistics of McGraw and WongJournal of Educational and Behavioral Statistics, 2000
- Reliance on correlation data for complexity metric use and validationACM SIGPLAN Notices, 1991