Exception handling is an important built-in feature of many modern programming languages such as Java. It allows developers to deal with abnormal or unexpected conditions by try-catch blocks that may occur at runtime in advance. Missing or improper implementation of exception handling can cause catastrophic consequences such as system crash. However, previous studies reveal that developers are unwilling or feel it hard to adopt exception handling mechanism, and tend to ignore it until a system failure forces them to do so. To help developers with exception handling, existing work produces recommendations such as code examples and exception types, which still requires developers to localize the try blocks and modify the catch block code to fit the context. In this paper, we propose a novel neural approach for automated exception handling, which can predict locations of try blocks and automatically generate the complete catch blocks. We collect a large number of Java methods from GitHub and conduct experiments to evaluate our approach. The evaluation results, including quantitative measurement and human evaluation, show that our approach is highly effective and outperforms all baselines. Our work makes one step further towards automated exception handling.

Jian Zhang

Beihang University

Xu Wang

Beihang University

China

Hongyu Zhang

University of Newcastle, Australia

Australia

Hailong Sun

Beihang University

Yanjun Pu

Beihang University

Xudong Liu

Beihang University

Long build times in continuous integration (CI) can greatly increase the cost in human and computing resources, and thus become a common barrier faced by software organizations adopting CI. Build outcome prediction has been proposed as one of the remedies to reduce such cost. However, the state-of-the-art approaches have a poor prediction performance for failed builds, and are not designed for practical usage scenarios. To address the problems, we first conduct an empirical study on 2,590,917 builds to characterize builds times in real-world projects, and a survey with 75 developers to understand their perceptions about build outcome prediction. Then, motivated by our study and survey results, we propose a new history-aware approach, named BuildFast, to predict CI build outcomes cost-efficiently and practically. It can help to obtain fast integration feedback and reduce integration cost. In particular, we introduce multiple failure-specific features from closely related historical builds via analyzing build logs and changed files, and propose an adaptive prediction model to switch between two models based on the build outcome of the previous build. We also investigate a practical online usage scenario of BuildFast, where builds are predicted in chronological order, and measure the benefit from correct predictions and the cost from incorrect predictions. Our experiments on 20 projects have demonstrated that BuildFast can improve the state-of-the-art approach by 47.5% in F1-score for failed builds.

Bihuan Chen

Fudan University, China

China

Linlin Chen

Fudan University

Chen Zhang

Fudan University

Xin Peng

Fudan University, China

China

Using open-source libraries can provide rich functions and reduce development cost. However, some critical issues have also been caused such as license conflicts and vulnerability risks. In this paper, we design and implement an open-source libraries detection tool OSLDetector which uses methods of matching features to detect third-party libraries for multi-platform software in binaries. We took a series of methods such as filtering features and novelty building an internal clone forest to cope with the challenge of feature duplication. The tool can also provide the conflict of licenses and identify possible corresponding vulnerabilities, so these potential risks can be resolved and avoided. To evaluate the efficiency of OSLDetector, we collect 5K libraries containing 9K versions and manage their respective license type and existing vulnerabilities. The experimental results with a precision of 96\% and recall of 92.3\% show that OSLDetector is effective and outperforms similar tools.

Dan Zhang

Tsinghua University