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@en

Compiler correctness is a cornerstone of reliable software development. However, systematic testing of compilers is infeasible, given the vast space of possible programs and the complexity of modern programming languages. In this context, differential testing offers a practical methodology as it addresses the oracle problem by comparing the output of alternative compilers given the same set of programs as input. In this paper, we investigate the effectiveness of differential testing in finding bugs within the Kotlin compilers developed at JetBrains. We propose a black-box generative approach that creates input programs for the K1 and K2 compilers. First, we build workable models of Kotlin semantic (semantic interface) and syntactic (enriched context-free grammar) language features, which are subsequently exploited to generate random code snippets. Second, we extend random sampling by introducing two genetic algorithms (GAs) that aim to generate more diverse input programs. Our case study shows that the proposed approach effectively detects bugs in K1 and K2; these bugs have been confirmed and (some) fixed by JetBrains developers. While we do not observe a significant difference w.r.t. the number of defects uncovered by the different search algorithms, random search and GAs are complementary as they find different categories of bugs. Finally, we provide insights into the relationships between the size, complexity, and fault detection capability of the generated input programs.@en