To speed up the bug-fixing process, it is essential to triage bugs into the right components as soon as possible. Given the large number of bugs filed everyday, a reliable and effective bug-component triaging tool is needed to assist this task. LR-BKG is the state-of-the-art toolkit for doing this. However, the suboptimal performance for recommending the right component at the first position (low Top-1 accuracy) limits its usage in practice. We thoroughly investigate the limitations of LR-BKG and find out the gap between the manual feature design of LR-BKG and the characteristics of bug reports causes such suboptimal performance. Therefore, we propose an approach, DEEPTRIAG, which uses the large scale pre-trained models to extract deep features automatically from bug reports (including bug summary and description), to fill this gap. DEEPTRIAG transforms bug-component triaging into a multi-classification task (CodeBERT-Classifier) and a generation task (CodeT5-Generator). Then, we ensemble the prediction results from them to improve the performance of bug-component triaging further. Extensive experimental results demonstrate the superior performance of DEEPTRIAG on bug-component triaging over LR-BKG. In particular, the overall Top-1 accuracy is improved from 56.2% to 68.3% on Mozilla dataset and from 51.3% to 64.1% on Eclipse dataset, which verifies the effectiveness and generalization of our approach on improving the practical usage for bug-component triaging.

Exploratory testing is an effective testing approach which leverages the tester’s knowledge and creativity to design test cases to provoke and recognize failures at the system level from the end user’s perspective. Although some principles and guidelines have been proposed to guide exploratory testing, there are no effective tools for automatic generation of exploratory test scenarios (a.k.a soap opera tests). Existing test generation techniques rely on specifications, program differences and fuzzing, which are not suitable for exploratory test generation. In this paper, we propose to leverage the scenario and oracle knowledge in bug reports to generate soap opera test scenarios. We develop open information extraction methods to construct a system knowledge graph (KG) of user tasks and failures from the steps to reproduce, expected results and observed results in bug reports. We construct a proof-of-concept KG from 25,939 bugs of the Firefox browser. Our evaluation shows the constructed KG is of high quality. Based on the KG, we create soap opera test scenarios by combining the scenarios of relevant bugs, and develop a web tool to present the created test scenarios and support exploratory testing. In our user study, 5 users find 18 bugs from 5 seed bugs in 2 hours using our tool, while the control group finds only 5 bugs based on the recommended similar bugs.