- author = "Aymeric Blot and Justyna Petke",
- title = "A Comprehensive Survey of Benchmarks for Automated Improvement of Software's Non-Functional Properties",
- howpublished = "arXiv",
- year = "2022",
- month = "16 " # dec,
- keywords = "genetic algorithms, genetic programming, genetic improvement, software performance, non-functional properties, benchmark",
- eprint = "2212.08540",
- archiveprefix = "arXiv",
- primaryclass = "cs.SE",
-
URL = "
https://arxiv.org/abs/2212.08540",
-
data_url = "https://bloa.github.io/nfunc_survey/",
- size = "43 pages",
-
abstract = "Performance is a key quality of modern software.
Although recent years have seen a spike in research on
automated improvement of software's execution time,
energy, memory consumption, etc., there is a noticeable
lack of standard benchmarks for such work. It is also
unclear how such benchmarks are representative of
current software. Furthermore, frequently
non-functional properties of software are targeted for
improvement one-at-a-time, neglecting potential
negative impact on other properties.
In order to facilitate more research on automated improvement of non-functional properties of software, we conducted a survey gathering benchmarks used in previous work. We considered 5 major online repositories of software engineering work: ACM Digital Library, IEEE Xplore, Scopus, Google Scholar, and ArXiV. We gathered 5000 publications (3749 unique), which were systematically reviewed to identify work that empirically improves non-functional properties of software. We identified 386 relevant papers.
We find that execution time is the most frequently targeted property for improvement (in 62 percent of relevant papers), while multi-objective improvement is rarely considered (5 percent). Static approaches are prevalent (in 53 percent of papers), with exploratory approaches (evolutionary in 18 percent and non-evolutionary in 14 percent of papers) increasingly popular in the last 10 years. Only 40 percent of 386 papers describe work that uses benchmark suites, rather than single software, of those SPEC is most popular (covered in 33 papers). We also provide recommendations for choice of benchmarks in future work, noting, e.g., lack of work that covers Python or JavaScript. We provide all programs found in the 386 papers on our dedicated web page https://bloa.github.io/nfunc_survey/
We hope that this effort will facilitate more research on the topic of automated improvement of software's non-functional properties.",
