The Slow and The Furious? Performance Antipattern Detection in Cyber-Physical Systems

van Dinten, I.; Derakhshanfar, Pouria; Panichella, A.; Zaidman, A.E.

doi:10.1016/j.jss.2023.111904

The Slow and The Furious? Performance Antipattern Detection in Cyber-Physical Systems

Title

The Slow and The Furious? Performance Antipattern Detection in Cyber-Physical Systems

Author

van Dinten, I. (TU Delft Software Engineering)
Derakhshanfar, Pouria (JetBrains Research)
Panichella, A. (TU Delft Software Engineering)
Zaidman, A.E. (TU Delft Software Technology)

Department

Software Technology

Date

2024

Abstract

Cyber-Physical Systems (CPSs) have gained traction in recent years. A major non-functional quality of CPS is performance since it affects both usability and security. This critical quality attribute depends on the specialized hardware, simulation engines, and environmental factors that characterize the system under analysis. While a large body of research exists on performance issues in general, studies focusing on performance-related issues for CPSs are scarce. The goal of this paper is to build a taxonomy of performance issues in CPSs. To this aim, we present two empirical studies aimed at categorizing common performance issues (Study I) and helping developers detect them (Study II). In the first study, we examined commit messages and code changes in the history of 14 GitHub-hosted open-source CPS projects to identify commits that report and fix self-admitted performance issues. We manually analyzed 2699 commits, labeled them, and grouped the reported performance issues into antipatterns. We detected instances of three previously reported Software Performance Antipatterns (SPAs) for CPSs. Importantly, we also identified new SPAs for CPSs not described earlier in the literature. Furthermore, most performance issues identified in this study fall into two new antipattern categories: Hard Coded Fine Tuning (399 of 646) and Magical Waiting Number (150 of 646). In the second study, we introduce static analysis techniques for automatically detecting these two new antipatterns; we implemented them in a tool called AP-Spotter. We analyzed 9 open-source CPS projects not utilized to build the SPAs taxonomy to benchmark AP-Spotter. Our results show that AP-Spotter achieves 62.04% precision in detecting the antipatterns

Subject

Software performance antipatterns
Cyber-Physical Systems
Antipattern Detection
Software Maintenance
Empirical Software Engineering
Static Analysis

To reference this document use:

http://resolver.tudelft.nl/uuid:accfe314-6066-4eb5-ad14-04fad3e0f168

DOI

https://doi.org/10.1016/j.jss.2023.111904

ISSN

0164-1212

Source

Journal of Systems and Software, 210

Part of collection

Institutional Repository