RavenBuild: Context, Relevance, and Dependency Aware Build Outcome Prediction
Continuous Integration (CI) is a common practice adopted by modern software organizations. It plays an especially important role for large corporations like Company Y, where thousands of build jobs are submitted daily. Indeed, the cadence of development progress is constrained by the pace at which CI services process build jobs. To provide faster CI feedback, recent work explores how build outcomes can be anticipated. Although early results show plenty of promise, the distinct characteristics of Project X—a AAA video game project at Company Y, present new challenges for build outcome prediction. In the Project X setting, changes that do not modify source code also incur build failures. Moreover, we find that the code changes that have an impact that crosses the source-data boundary are more prone to build failures than code changes that do not impact data files. Since such changes are not fully characterized by the existing set of build outcome prediction features, state-of-the-art models tend to underperform.
Therefore, to accommodate the data context into build outcome prediction, we propose RavenBuild, a novel approach that leverages context, relevance, and dependency-aware features. We apply the state-of-the-art BuildFast model and RavenBuild to Project X, and observe that RavenBuild improves the F1-score of the failing class by 46%, the recall of the failing class by 76%, and AUC by 28%. To ease adoption in settings with heterogeneous project sets, we also provide a simplified alternative RavenBuild-CR, which excludes dependency-aware features. We apply RavenBuild-CR on 22 open-source projects and Project X, and observe across-the-board improvements as well. On the other hand, we find that a naive Parrot approach, which simply echoes the previous build outcome as its prediction, is surprisingly competitive with BuildFast and RavenBuild. Though Parrot fails to predict when the build outcome differs from their immediate predecessor, Parrot serves well as a tendency indicator of the sequences in build outcome datasets. Therefore, future studies should also consider comparing to the Parrot approach as a baseline when evaluating build outcome prediction models.
Fri 19 JulDisplayed time zone: Brasilia, Distrito Federal, Brazil change
10:30 - 11:00 | |||
10:30 30mPoster | Understanding the Impact of APIs Behavioral Breaking Changes on Client Applications Posters Dhanushka Jayasuriya University of Auckland, Valerio Terragni University of Auckland, Jens Dietrich Victoria University of Wellington, Kelly Blincoe University of Auckland | ||
10:30 30mPoster | Your Code Secret Belongs to Me: Neural Code Completion Tools Can Memorize Hard-coded Credentials Posters Yizhan Huang The Chinese University of Hong Kong, Yichen LI The Chinese University of Hong Kong, Weibin Wu Sun Yat-sen University, Jianping Zhang The Chinese University of Hong Kong, Michael Lyu The Chinese University of Hong Kong | ||
10:30 30mPoster | Natural Is The Best: Model-Agnostic Code Simplification for Pre-trained Large Language Models Posters Yan Wang Central University of Finance and Economics, Xiaoning Li Central University of Finance and Economics, Tien N. Nguyen University of Texas at Dallas, Shaohua Wang Central University of Finance and Economics, Chao Ni School of Software Technology, Zhejiang University, Ling Ding Central University of Finance and Economics | ||
10:30 30mPoster | PyRadar: Towards Automatically Retrieving and Validating Source Code Repository Information for PyPI Packages Posters Kai Gao Peking University, Weiwei Xu Peking University, Wenhao Yang Peking University, Minghui Zhou Peking University | ||
10:30 30mPoster | "The Law Doesn’t Work Like a Computer": Exploring Software Licensing Issues Faced by Legal Practitioners Posters Nathan Wintersgill William & Mary, Trevor Stalnaker William & Mary, Laura A. Heymann William & Mary, Oscar Chaparro William & Mary, Denys Poshyvanyk William & Mary | ||
10:30 30mPoster | RavenBuild: Context, Relevance, and Dependency Aware Build Outcome Prediction Posters Gengyi Sun University of Waterloo, Sarra Habchi Ubisoft Montréal, Shane McIntosh University of Waterloo | ||
10:30 30mPoster | MirrorFair: Fixing Fairness Bugs in Machine Learning Software via Counterfactual Predictions Posters Ying Xiao King's College London / Southern University of Science and Technology, Jie M. Zhang King's College London, Yepang Liu Southern University of Science and Technology, Mohammad Reza Mousavi King's College London, Sicen Liu Southern University of Science and Technology, Dingyuan Xue Southern University of Science and Technology | ||
10:30 30mPoster | Do Code Generation Models Think Like Us? - A Study of Attention Alignment between Large Language Models and Human Programmers Posters Bonan Kou Purdue University, Shengmai Chen Purdue University, Zhijie Wang University of Alberta, Lei Ma The University of Tokyo & University of Alberta, Tianyi Zhang Purdue University | ||
10:30 30mPoster | Dependency-Induced Waste in Continuous Integration: An Empirical Study on NPM Dependencies Posters Nimmi Weeraddana University of Waterloo, Mahmoud Alfadel University of Waterloo, Shane McIntosh University of Waterloo | ||
10:30 30mPoster | A Miss Is as Good as A Mile: Metamorphic Testing for Deep Learning Operators Posters Jinyin Chen Zhejiang University of Technology, Chengyu Jia Zhejiang University of Technology, Yunjie Yan Zhejiang University of Technology, Jie Ge Zhejiang University of Technology, haibin zheng Zhejiang University of Technology, Yao Cheng TÜV SÜD Asia Pacific Pte. Ltd. | ||
10:30 30mPoster | Investigating Documented Privacy Changes in Android OS Posters Chuan Yan University of Queensland, Mark Huasong Meng National University of Singapore, Fuman Xie University of Queensland, Guangdong Bai University of Queensland | ||
10:30 30mPoster | Analyzing Quantum Programs with LintQ: A Static Analysis Framework for Qiskit Posters | ||
10:30 30mPoster | Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions Posters Tao Xiao Nara Institute of Science and Technology, Hideaki Hata Shinshu University, Christoph Treude Singapore Management University, Kenichi Matsumoto Nara Institute of Science and Technology | ||
10:30 30mPoster | Bloat beneath Python's Scales: A Fine-Grained Inter-Project Dependency Analysis Posters Georgios-Petros Drosos ETH Zurich, Thodoris Sotiropoulos ETH Zurich, Diomidis Spinellis Athens University of Economics and Business & Delft University of Technology, Dimitris Mitropoulos University of Athens |
This room is conjoined with the Foyer to provide additional space for the coffee break, and hold poster presentations throughout the event.