Registered user since Sun 19 Aug 2018
He is a full professor and the director of the iSE laboratory (http://www.iselab.cn/) in Nanjing University. He is the founder of mooctest.com.
Contributions
Unit testing is a critical part of software development process, ensuring the correctness of basic programming units in a program (e.g., a method). Search-based software testing (SBST) is an automated approach to generating test cases. SBST generates test cases with genetic algorithms by specifying the coverage criterion (e.g., branch coverage). However, a good test suite must have different properties, which cannot be captured by using an individual coverage criterion. Therefore, the state-of-the-art approach combines multiple criteria to generate test cases. As combining multiple coverage criteria brings multiple objectives for optimization, it hurts the test suites’ coverage for certain criteria compared with using the single criterion. To cope with this problem, we propose a novel approach named \textbf{smart selection}. Based on the coverage correlations among criteria and the coverage goals’ subsumption relationships, smart selection selects a subset of coverage goals to reduce the number of optimization objectives and avoid missing any properties of all criteria. We conduct experiments to evaluate smart selection on $400$ Java classes with three state-of-the-art genetic algorithms. On average, smart selection outperforms combining all goals on $65.1%$ of the classes having significant differences between the two approaches.
DOIResearch Papers
Tue 11 Oct 2022 14:40 - 15:00 at Banquet B - Technical Session 7 - Fuzzing II Chair(s): Karine Even-MendozaThe tremendous advancements in deep learning techniques have empowered question answering(QA) systems with the capability of dealing with various tasks. Many commercial QA systems, such as Siri, Google Home, and Alexa, have been deployed to assist people in different daily activities. However, modern QA systems are often designed to deal with different topics and task formats, which makes both the test collection and labeling tasks difficult and thus threats their quality.
To alleviate this challenge, in this paper, we design and implement a fuzzing framework for QA systems, namely QATest, based on the metamorphic testing theory. It provides the first uniform solution to generate tests with oracle information automatically for various QA systems, such as machine reading comprehension, open-domain QA, and QA on knowledge bases. To further improve testing efficiency and generate more tests detecting erroneous behaviors, we design N-gram coverage criteria and perplexity priority based on the features of the question data to guide the generation process. To evaluate the performance of QATest, we experiment it on four QA systems that are designed for different tasks. The experiment results show that the tests generated by QATest detect hundreds of erroneous behaviors of QA systems efficiently. Also, the results confirm that the testing criteria can improve test diversity and fuzzing efficiency.
Research Papers
Wed 12 Oct 2022 17:40 - 18:00 at Banquet A - Technical Session 18 - Testing II Chair(s): Darko MarinovUnit testing is a critical part of the software development process, ensuring the correctness of basic programming units in a program (e.g., a method). Search-based software testing (SBST) is an automated approach to generating test cases. SBST generates test cases with the genetic algorithms by specifying the coverage criterion (e.g., branch coverage). However, a good test suite must have different properties, which cannot be captured by using an individual coverage criterion alone. Therefore, the state-of-the-art approach combines multiple criteria to generate test cases. As combining multiple coverage criteria brings multiple objectives for optimization, it hurts the test suites’ coverage for certain criteria compared with using the single criterion. To cope with this, we propose a novel approach named \textbf{smart selection}. Based on the coverage correlations among criteria and the coverage goals’ subsumption relationships, smart selection selects a subset of coverage goals to reduce the number of optimization objectives and avoid missing any properties of all criteria. We conduct experiments to evaluate smart selection on $400$ Java classes with three state-of-the-art genetic algorithms. On average, smart selection outperforms the original combination (i.e., combining all goals) on $77$ Java classes, accounting for $65.1%$ of the classes having significant differences between the two approaches.
DOI Pre-print