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

Just-In-Time (JIT) defect prediction is a classification model that is trained using historical data to predict bug-introducing changes. However, recent studies raised concerns related to the explainability of the predictions of many software analytics applications (i.e., practitioners do not understand why commits are risky and how to improve them). In addition, the adoption of Just-In-Time defect prediction is still limited due to a lack of integration into CI/CD pipelines and modern software development platforms (e.g., GitHub). In this paper, we present an explainable Just-In-Time defect prediction framework to automatically generate feedback to developers by providing the riskiness of each commit, explaining why such commit is risky, and suggesting risk mitigation plans. The proposed framework is integrated into the GitHub CI/CD pipeline as a GitHub application to continuously monitor and analyse a stream of commits in many GitHub repositories. Finally, we discuss the usage scenarios and their implications to practitioners. The VDO demonstration is available at https://youtu.be/HJBzULrS6hE.

Chaiyakarn Khanan

Mahidol University

Worawit Luewichana

Mahidol University

Krissakorn Pruktharathikoon

Mahidol University

Jirayus Jiarpakdee

Monash University, Australia

Chakkrit (Kla) Tantithamthavorn

Monash University, Australia

Australia

Morakot Choetkiertikul

Mahidol University, Thailand

Thailand

Chaiyong Rakhitwetsagul

Mahidol University, Thailand

Thailand

Thanwadee Sunetnanta

Mahidol University

This paper presents AirMochi, a tool that provides remote access and control of apps by leveraging a mobile platform’s publicly exported \emph{accessibility features}. While AirMochi is designed to be platform-independent, we discuss its iOS implementation. We show that AirMochi places no restrictions on apps, is able to handle a variety of scenarios, and imposes a negligible performance overhead. https://youtu.be/rhPz2Hs4Ius https://github.com/nkllkc/air_mochi

Nikola Lukic

University of Southern California

United States

Saghar Talebipour

University of Southern California

Nenad Medvidović

University of Southern California, USA

United States

Recognition of human behaviours including body motions and facial expressions plays a significant role in human-centric software engineering. However, due to the data and computation intensive nature of human behaviour recognition through video analytics, expensive powerful machines are often required, which could hinder the research and application in human-centric software engineering. To address such an issue, this paper proposes a cost-effective human behaviour recognition system named Edge4Real which can be easily deployed in an edge computing environment with commodity machines. Compared with existing centralised solutions, Edge4Real has three major advantages including cost-effectiveness, easy-to-use, and real-time. Specifically, Edge4Real adopts a distributed architecture where components such as motion capturing, human behaviour recognition, data decoding and extraction, and the application of the recognition result, can be deployed on separated end devices and edge nodes in an edge computing environment. Using a virtual reality application which can capture a user’s motion and translate into the motion of a 3D avatar in real time, we successfully validate the effectiveness of the system and demonstrate its promising value to the research and application of human-centric software engineering. The demo video can be found at https://youtu.be/tnEshD8j-kA.

DI SHAO

School of Information Technology, Deakin University

Xiao Liu

School of Information Technology, Deakin University

Australia

Ben Cheng

School of Information Technology, Deakin University

Owen Wang

School of Information Technology, Deakin University

Thuong Hoang

School of Information Technology, Deakin University

More and more new technologies are used in test development. Among them, automatic test generation, a promising technology to improve the efficiency of unit testing, currently performs not satisfactory in practice. Test recommendation, like code recommendation, is another feasible technology for supporting efficient unit testing and gets more and more attention. In this paper, we develop a novel system, namely HomoTR, which implements online test recommendations by measuring the homology of two methods. If the new method under test shares homology with an existing method that has tests, HomoTR would recommend the tests to the new method. The preliminary experiments show that HomoTR can quickly and effectively recommend test cases to help the testers improve the testing efficiency. Besides, HomoTR has been integrated into the MoocTest platform successfully, so it can also execute the recommended tests automatically and visualize the testing results (e.g., Branch Coverage) friendly to help testers understand the process of testing. The demo video of HomoTR can be found at {\color{blue}\url{https://youtu.be/_227EfcUbus}}.

Chenqian Zhu

Nanjing University

Weisong Sun

State Key Laboratory for Novel Software Technology, Nanjing University

China

Qin LIU

Yangyang Yuan

Nanjing University

Chunrong Fang

Nanjing University, China

China

Yong Huang

State Key Laboratory for Novel Software Technology, Nanjing University

China

WebAssembly is a new programming language built for better performance in web applications. It defines a binary code format and a text representation for the code. At first glance, WebAssembly files are not easily understandable to human readers, regardless of the experience level. As a result, distributed third-party WebAssembly modules need to be implicitly trusted by developers as verifying the functionality requires significant effort. To this end, we develop an automated classification tool WASim for identifying the purpose of WebAssembly programs by analyzing features at the module-level. It assigns purpose labels to a module in order to assist developers in understanding the binary module. The code for WASim is available at https://github.com/WASimilarity/WASim and a video demo is available at https://youtu.be/usfYFIeTy0U.

Alan Romano

University at Buffalo, SUNY

United States

Weihang Wang

University at Buffalo, SUNY

United States