{"title":"PACE: A Program Analysis Framework for Continuous Performance Prediction","authors":"Chidera Biringa, Gökhan Kul","doi":"10.1145/3637230","DOIUrl":null,"url":null,"abstract":"<p>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 paper presents <monospace>PACE</monospace>, 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.</p>","PeriodicalId":50933,"journal":{"name":"ACM Transactions on Software Engineering and Methodology","volume":"2 1","pages":""},"PeriodicalIF":6.6000,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Software Engineering and Methodology","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3637230","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
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 paper 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.
软件开发团队建立了包含自动测试案例的精心设计的持续集成管道，以加快软件开发进程。自动化测试有助于验证代码修改的正确性，从而缩短对需求变化的响应时间。然而，如果软件团队不跟踪待处理修改对性能的影响，他们可能需要花费大量时间重构现有代码。本文介绍的 PACE 是一个程序分析框架，可持续反馈待处理代码更新对性能的影响。我们通过映射代码更新时功能测试用例的执行时间来设计性能微基准。我们将微基准映射到代码风格测量特征，并将其输入预测器进行性能预测。我们的实验在预测代码性能方面取得了显著的成绩，在神经呈现的代码风格测量特征方面比目前最先进的技术高出 75%。
期刊介绍：
Designing and building a large, complex software system is a tremendous challenge. ACM Transactions on Software Engineering and Methodology (TOSEM) publishes papers on all aspects of that challenge: specification, design, development and maintenance. It covers tools and methodologies, languages, data structures, and algorithms. TOSEM also reports on successful efforts, noting practical lessons that can be scaled and transferred to other projects, and often looks at applications of innovative technologies. The tone is scholarly but readable; the content is worthy of study; the presentation is effective.
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