{"title":"A Comprehensive Survey of Benchmarks for Improvement of Software's Non-Functional Properties","authors":"Aymeric Blot, Justyna Petke","doi":"10.1145/3711119","DOIUrl":null,"url":null,"abstract":"Despite recent increase in research on improvement of non-functional properties of software, such as energy usage or program size, there is a lack of standard benchmarks for such work. This absence hinders progress in the field, and raises questions about the representativeness of current benchmarks of real-world software. To address these issues and facilitate further research on improvement of non-functional properties of software, we conducted a comprehensive survey on the benchmarks used in the field thus far. We searched five major online repositories of research work, collecting 5499 publications (4066 unique), and systematically identified relevant papers to construct a rich and diverse corpus of 425 relevant studies. We find that execution time is the most frequently improved property in research work (63%), while multi-objective improvement is rarely considered (7%). Static approaches for improvement of non-functional software properties are prevalent (51%), with exploratory approaches (18% evolutionary and 15% non-evolutionary) increasingly popular in the last 10 years. Only 39% of the 425 papers describe work that uses benchmark suites, rather than single software, of those SPEC is most popular (63 papers). We also provide recommendations for future work, noting, for instance, lack of benchmarks for non-functional improvement that covers Python, JavaScript, or mobile devices. All the details regarding the 425 identified papers are available on our dedicated webpage: https://bloa.github.io/nfunc_survey.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"20 1","pages":""},"PeriodicalIF":23.8000,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Computing Surveys","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3711119","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Despite recent increase in research on improvement of non-functional properties of software, such as energy usage or program size, there is a lack of standard benchmarks for such work. This absence hinders progress in the field, and raises questions about the representativeness of current benchmarks of real-world software. To address these issues and facilitate further research on improvement of non-functional properties of software, we conducted a comprehensive survey on the benchmarks used in the field thus far. We searched five major online repositories of research work, collecting 5499 publications (4066 unique), and systematically identified relevant papers to construct a rich and diverse corpus of 425 relevant studies. We find that execution time is the most frequently improved property in research work (63%), while multi-objective improvement is rarely considered (7%). Static approaches for improvement of non-functional software properties are prevalent (51%), with exploratory approaches (18% evolutionary and 15% non-evolutionary) increasingly popular in the last 10 years. Only 39% of the 425 papers describe work that uses benchmark suites, rather than single software, of those SPEC is most popular (63 papers). We also provide recommendations for future work, noting, for instance, lack of benchmarks for non-functional improvement that covers Python, JavaScript, or mobile devices. All the details regarding the 425 identified papers are available on our dedicated webpage: https://bloa.github.io/nfunc_survey.
期刊介绍:
ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods.
ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.