Write a Blog >>
ASE 2020
Mon 21 - Fri 25 September 2020 Melbourne, Australia
Wed 23 Sep 2020 00:20 - 00:40 at Wombat - Testing (1) Chair(s): Lingming Zhang

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 Sep
Times are displayed in time zone: (UTC) Coordinated Universal Time change

00:00 - 01:00
Testing (1)Research Papers / Tool Demonstrations at Wombat
Chair(s): Lingming ZhangUniversity of Illinois at Urbana-Champaign, USA
MockSniffer: Characterizing and Recommending Mocking Decisions for Unit Tests
Research Papers
Hengcheng ZhuSouthern University of Science and Technology, Lili WeiThe Hong Kong University of Science and Technology, Ming WenHuazhong University of Science and Technology, China, Yepang LiuSouthern University of Science and Technology, Shing-Chi CheungHong Kong University of Science and Technology, China, Qin ShengWeBank Co Ltd, Cui ZhouWeBank Co Ltd
DOI Pre-print
Defect Prediction Guided Search-Based Software Testing
Research Papers
Anjana PereraMonash University, Aldeida AletiMonash University, Marcel BöhmeMonash University, Australia, Burak TurhanMonash University
DOI Pre-print
STIFA: Crowdsourced Mobile Testing Report Selection Based on Text and Image Fusion Analysis
Tool Demonstrations
Zhenfei CaoNanjing University, Xu WangNanjing University, Shengcheng YuNanjing University, China, Yexiao YunNanjing University, Chunrong FangNanjing University, China