PACE: A Program Analysis Framework for Continuous Performance Prediction
Software development teams establish elaborate continuous integration pipelines containing automated test cases to accelerate the development process of software. Automated tests help to verify the correctness of code modifications decreasing the response time to changing requirements. However, when the software teams do not track the performance impact of pending modifications, they may need to spend considerable time refactoring existing code. This article presents PACE, a program analysis framework that provides continuous feedback on the performance impact of pending code updates. We design performance microbenchmarks by mapping the execution time of functional test cases given a code update. We map microbenchmarks to code stylometry features and feed them to predictors for performance predictions. Our experiments achieved significant performance in predicting code performance, outperforming current state-of-the-art by 75% on neural-represented code stylometry features.
Fri 2 MayDisplayed time zone: Eastern Time (US & Canada) change
11:00 - 12:30 | Program Comprehension 3Research Track / Journal-first Papers at 204 Chair(s): Arie van Deursen TU Delft | ||
11:00 15mTalk | Automated Test Generation For Smart Contracts via On-Chain Test Case Augmentation and MigrationBlockchain Research Track Jiashuo Zhang Peking University, China, Jiachi Chen Sun Yat-sen University, John Grundy Monash University, Jianbo Gao Peking University, Yanlin Wang Sun Yat-sen University, Ting Chen University of Electronic Science and Technology of China, Zhi Guan Peking University, Zhong Chen Pre-print | ||
11:15 15mTalk | Boosting Code-line-level Defect Prediction with Spectrum Information and Causality Analysis Research Track Shiyu Sun , Yanhui Li Nanjing University, Lin Chen Nanjing University, Yuming Zhou Nanjing University, Jianhua Zhao Nanjing University, China | ||
11:30 15mTalk | BatFix: Repairing language model-based transpilation Journal-first Papers Daniel Ramos Carnegie Mellon University, Ines Lynce INESC-ID/IST, Universidade de Lisboa, Vasco Manquinho INESC-ID; Universidade de Lisboa, Ruben Martins Carnegie Mellon University, Claire Le Goues Carnegie Mellon University | ||
11:45 15mTalk | Tracking the Evolution of Static Code Warnings: The State-of-the-Art and a Better Approach Journal-first Papers | ||
12:00 15mTalk | PACE: A Program Analysis Framework for Continuous Performance Prediction Journal-first Papers | ||
12:15 15mTalk | Mimicking Production Behavior With Generated Mocks Journal-first Papers Deepika Tiwari KTH Royal Institute of Technology, Martin Monperrus KTH Royal Institute of Technology, Benoit Baudry Université de Montréal | ||