Defect Prediction Guided Search-Based Software Testing
Today, most automated test generators, such as search-based software testing (SBST) techniques focus on achieving high code coverage. However, high code coverage is not sufficient to maximise the number of bugs found, especially when given a limited testing budget. In this paper, we propose an automated test generation technique that is also guided by the estimated degree of defectiveness of the source code. Parts of the code that are likely to be more defective receive more testing budget than the less defective parts. To measure the degree of defectiveness, we leverage Schwa, a notable defect prediction technique.
We implement our approach into EvoSuite, a state of the art SBST tool for Java. Our experiments on the Defects4J benchmark demonstrate the improved efficiency of defect prediction guided test generation and confirm our hypothesis that spending more time budget on likely defective parts increases the number of bugs found in the same time budget.
Wed 23 SepDisplayed time zone: (UTC) Coordinated Universal Time change
00:00 - 01:00
Testing (1)Research Papers / Tool Demonstrations at Wombat
Chair(s): Lingming Zhang University of Illinois at Urbana-Champaign, USA
|MockSniffer: Characterizing and Recommending Mocking Decisions for Unit Tests|
Hengcheng Zhu Southern University of Science and Technology, Lili Wei The Hong Kong University of Science and Technology, Ming Wen Huazhong University of Science and Technology, China, Yepang Liu Southern University of Science and Technology, Shing-Chi Cheung Hong Kong University of Science and Technology, China, Qin Sheng WeBank Co Ltd, Cui Zhou WeBank Co LtdDOI Pre-print
|Defect Prediction Guided Search-Based Software Testing|
Anjana Perera Monash University, Aldeida Aleti Monash University, Marcel Böhme Monash University, Australia, Burak Turhan Monash UniversityDOI Pre-print
|STIFA: Crowdsourced Mobile Testing Report Selection Based on Text and Image Fusion Analysis|
Zhenfei Cao Nanjing University, Xu Wang Nanjing University, Shengcheng Yu Nanjing University, China, Yexiao Yun Nanjing University, Chunrong Fang Nanjing University, China