{"title":"Statistical Reasoning About Programs","authors":"Marcel Bohme","doi":"10.1109/icse-nier55298.2022.9793535","DOIUrl":null,"url":null,"abstract":"We discuss the advent of a new program analysis paradigm that allows anyone to make precise statements about the behavior of programs as they run in production across hundreds and millions of machines or devices. The scale-oblivious, in vivo program analysis leverages an almost inconceivable rate of user-generated program executions across large fleets to analyze programs of arbitrary size and composition with negligible performance overhead. In this paper, we reflect on the program analysis problem, the prevalent paradigm, and the practical reality of program analysis at large software companies. We illustrate the new paradigm using several success stories and suggest a number of exciting new research directions.","PeriodicalId":416186,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"230 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icse-nier55298.2022.9793535","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
We discuss the advent of a new program analysis paradigm that allows anyone to make precise statements about the behavior of programs as they run in production across hundreds and millions of machines or devices. The scale-oblivious, in vivo program analysis leverages an almost inconceivable rate of user-generated program executions across large fleets to analyze programs of arbitrary size and composition with negligible performance overhead. In this paper, we reflect on the program analysis problem, the prevalent paradigm, and the practical reality of program analysis at large software companies. We illustrate the new paradigm using several success stories and suggest a number of exciting new research directions.
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