{"title":"海报:自动修复真实世界的JavaScript性能bug","authors":"Marija Selakovic, Michael Pradel","doi":"10.1109/ICSE.2015.260","DOIUrl":null,"url":null,"abstract":"Programs often suffer from poor performance that can be fixed by relatively simple changes. Currently, developers either manually identify and fix such performance problems, or they rely on compilers to optimize their code. Unfortunately, manually fixing performance bugs is non-trivial, and compilers are limited to a predefined set of optimizations. This paper presents an approach for automatically finding and fixing performance bugs in JavaScript programs. To focus our work on relevant problems, we study 37 real-world performance bug fixes from eleven popular JavaScript projects and identify several recurring fix patterns. Based on the results of the study, we present a static analysis that identifies occurrences of common fix patterns and a fix generation technique that proposes to transform a given program into a more efficient program. Applying the fix generation technique to three libraries with known performance bugs yields fixes that are equal or equivalent to those proposed by the developers, and that lead to speedups between 10% and 25%.","PeriodicalId":330487,"journal":{"name":"2015 IEEE/ACM 37th IEEE International Conference on Software Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Poster: Automatically Fixing Real-World JavaScript Performance Bugs\",\"authors\":\"Marija Selakovic, Michael Pradel\",\"doi\":\"10.1109/ICSE.2015.260\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Programs often suffer from poor performance that can be fixed by relatively simple changes. Currently, developers either manually identify and fix such performance problems, or they rely on compilers to optimize their code. Unfortunately, manually fixing performance bugs is non-trivial, and compilers are limited to a predefined set of optimizations. This paper presents an approach for automatically finding and fixing performance bugs in JavaScript programs. To focus our work on relevant problems, we study 37 real-world performance bug fixes from eleven popular JavaScript projects and identify several recurring fix patterns. Based on the results of the study, we present a static analysis that identifies occurrences of common fix patterns and a fix generation technique that proposes to transform a given program into a more efficient program. Applying the fix generation technique to three libraries with known performance bugs yields fixes that are equal or equivalent to those proposed by the developers, and that lead to speedups between 10% and 25%.\",\"PeriodicalId\":330487,\"journal\":{\"name\":\"2015 IEEE/ACM 37th IEEE International Conference on Software Engineering\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE/ACM 37th IEEE International Conference on Software Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSE.2015.260\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE/ACM 37th IEEE International Conference on Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSE.2015.260","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Programs often suffer from poor performance that can be fixed by relatively simple changes. Currently, developers either manually identify and fix such performance problems, or they rely on compilers to optimize their code. Unfortunately, manually fixing performance bugs is non-trivial, and compilers are limited to a predefined set of optimizations. This paper presents an approach for automatically finding and fixing performance bugs in JavaScript programs. To focus our work on relevant problems, we study 37 real-world performance bug fixes from eleven popular JavaScript projects and identify several recurring fix patterns. Based on the results of the study, we present a static analysis that identifies occurrences of common fix patterns and a fix generation technique that proposes to transform a given program into a more efficient program. Applying the fix generation technique to three libraries with known performance bugs yields fixes that are equal or equivalent to those proposed by the developers, and that lead to speedups between 10% and 25%.