Name
Abstract | ||
|---|---|---|
The test code, just like production source code, is subject to bad design and programming practices, also known as smells. The presence of test smells in a software project may affect the quality, maintainability, and extendability of test suites making them less effective in finding potential faults and quality issues in the project's production code. In this paper, we introduce tsDetect, an automated test smell detection tool for Java software systems that uses a set of detection rules to locate existing test smells in test code. We evaluate the effectiveness of tsDetect on a benchmark of 65 unit test files containing instances of 19 test smell types. Results show that tsDetect achieves a high detection accuracy with an average precision score of 96% and an average recall score of 97%. tsDetect is publicly available, with a demo video, at: https://testsmells.github.io/
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3368089.3417921 | ESEC/FSE '20: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Virtual Event
USA
November, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7043-1 | 2 |
PageRank | References | Authors |
0.37 | 20 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anthony Peruma | 1 | 28 | 5.80 |
| Khalid Almalki | 2 | 2 | 0.37 |
| Christian D. Newman | 3 | 66 | 11.50 |
| Mohamed Wiem Mkaouer | 4 | 228 | 28.58 |
| Ali Ouni | 5 | 16 | 8.44 |
| Fabio Palomba | 6 | 1027 | 54.58 |