A Comprehensive Survey of Benchmarks for Improvement of Software's Non-Functional Properties

IF 28 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2025-01-27 DOI:10.1145/3711119
Aymeric Blot, Justyna Petke
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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.
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软件非功能属性改进基准的全面调查
尽管最近在改进软件的非功能属性(如能源使用或程序大小)方面的研究有所增加,但缺乏此类工作的标准基准。这种缺失阻碍了该领域的进步,并引发了关于当前现实世界软件基准的代表性的问题。为了解决这些问题并促进对软件非功能属性改进的进一步研究,我们对迄今为止在该领域使用的基准进行了全面调查。我们检索了5个主要的在线研究著作库,收集了5499篇出版物(4066篇独特的),并系统地识别了相关论文,构建了一个丰富多样的425篇相关研究的语料库。我们发现,执行时间是研究工作中最常改进的属性(63%),而多目标改进很少被考虑(7%)。用于改进非功能性软件属性的静态方法很流行(51%),而探索性方法(18%是进化的,15%是非进化的)在过去10年中越来越流行。425篇论文中只有39%描述了使用基准套件而不是单一软件的工作,其中SPEC最受欢迎(63篇论文)。我们还为未来的工作提供了建议,例如,缺少针对Python、JavaScript或移动设备的非功能改进的基准测试。有关425篇论文的所有详细信息可在我们的专门网页上查阅:https://bloa.github.io/nfunc_survey。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
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.
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