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

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

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

Experience

Experience paper: Testing of mobile apps is time-consuming and requires a great deal of manual effort. For this reason, industry and academic researchers have proposed a number of test-input- generation techniques for automating app testing. Although useful, these techniques have weaknesses and limitations that often pre- vent them from achieving high coverage. We believe that one of the reasons for these limitations is that tool developers tend to focus mainly on improving the strategy the techniques employ to explore app behavior, whereas limited effort has been put into investigating other ways to improve the performance of these tech- niques. To address this problem, and get a better understanding of the limitations of input-generation techniques for mobile apps, we conducted an in-depth study of the limitations of Monkey–arguably the most widely used tool for automated testing of Android apps. Specifically, in our study, we manually analyzed Monkey’s perfor- mance on 68 benchmarks to identify the common limitations that prevent the tool from achieving better coverage results. We then assessed the coverage improvement that Monkey could achieve if these limitations were eliminated. In our analysis of the results, we also discuss whether other existing test-input-generation tools suffer from these common limitations and provide insights on how they could address them.

Farnaz Behrang

Georgia Tech

Alessandro Orso

Georgia Tech

United States

Automated testing of mobile apps has received significant attention in the recent years from researchers and practitioners alike. In this paper, we report on the largest empirical study to date, aimed at understanding the test automation culture prevalent among mobile app developers. We systematically examined more than 3.5 million repositories on GitHub and identified more than 12, 000 non-trivial and real-world Android apps. We then analyzed these non-trivial apps to investigate (1) the trends in adoption of test automation; (2) working habits of mobile app developers in regards to automated testing; and (3) the correlation between the adoption of test automation and the popularity of projects. Among others, we found that (1) only 8% of the mobile app development projects leverage automated testing practices; (2) developers tend to follow the same test automation practices across projects; and (3) popular projects, measured in terms of the number of contributors, stars, and forks on GitHub, are more likely to adopt test automation practices. To understand the rationale behind our observations, we further conducted a survey with 148 professional and experienced developers contributing to the subject apps. Our findings shed light on the current practices and future research directions pertaining to test automation for mobile app development.

Jun-Wei Lin

University of California, Irvine

United States

Navid Salehnamadi

University of California, Irvine

Sam Malek

University of California at Irvine, USA

United States

Most software systems interact with their environment extensively. This is especially true for mobile apps, whose behavior often depends on sensors, external services, and inter-process communications. These interactions can complicate testing activities, as test cases may need a complete environment to be executed. They can also cause issues such as flakiness, for example when the environment behaves in non-deterministic ways. For these reasons, it is common to create test mocks that can eliminate the need for (part of) the environment to be present during testing. Manual mock creation, however, can be extremely time consuming and error-prone. Moreover, the generated mocks can typically only be used in the context of the specific tests for which they were created. To address these issues, we propose MOKA, a general framework for collecting and generating reusable test mocks in an automated way. MOKA leverages the ability to observe a large number of interactions between an application and its environment and uses an iterative approach to generate mocks with different reusability characteristics—advanced mocks generated through program synthesis and basic record-replay-based mocks. In this paper, we describe the new ideas behind MOKA, its main characteristics, a preliminary study, and a set of possible applications that would benefit from our framework.

Mattia Fazzini

University of Minnesota

United States

Alessandra Gorla

IMDEA Software Institute

Spain

Alessandro Orso

Georgia Tech

United States