Detecting Performance Bugs Research Articles

Performance mutation testing

SummaryPerformance bugs are known to be a major threat to the success of software products. Performance tests aim to detect performance bugs by executing the program through test cases and checking whether it exhibits a noticeable performance degradation. The principles of mutation testing, a well‐established testing technique for the assessment of test suites through the injection of artificial faults, could be exploited to evaluate and improve the detection power of performance tests. However, the application of mutation testing to assess performance tests, henceforth called performance mutation testing (PMT), is a novel research topic with numerous open challenges. In previous papers, we identified some key challenges related to PMT. In this work, we go a step further and explore the feasibility of applying PMT at the source‐code level in general‐purpose languages. To do so, we revisit concepts associated with classical mutation testing and design seven novel mutation operators to model known bug‐inducing patterns. As a proof of concept, we applied traditional mutation operators as well as performance mutation operators to open‐source C++ programs. The results reveal the potential of the new performance‐mutants to help assess and enhance performance tests when compared with traditional mutants. A review of live mutants in these programs suggests that they can induce the design of special test inputs. In addition to these promising results, our work brings a whole new set of challenges related to PMT, which will hopefully serve as a starting point for new contributions in the area.

Performance mutation testing: Hypothesis and open questions

Performance bugs are common, costly, and elusive. Performance tests aim to detect performance bugs by running the program with specific inputs and determining whether the observed behaviour is acceptable. There not exist mechanisms, however, to assess the effectiveness of performance tests. Mutation testing is a technique to evaluate and enhance functional test suites by seeding artificial faults in the program under test. In this new idea paper, we explore the applicability of mutation testing to assess and improve performance tests. This novel approach is motivated with examples and open questions.

Understanding and detecting real-world performance bugs

Developers frequently use inefficient code sequences that could be fixed by simple patches. These inefficient code sequences can cause significant performance degradation and resource waste, referred to as performance bugs. Meager increases in single threaded performance in the multi-core era and increasing emphasis on energy efficiency call for more effort in tackling performance bugs. This paper conducts a comprehensive study of 110 real-world performance bugs that are randomly sampled from five representative software suites (Apache, Chrome, GCC, Mozilla, and MySQL). The findings of this study provide guidance for future work to avoid, expose, detect, and fix performance bugs. Guided by our characteristics study, efficiency rules are extracted from 25 patches and are used to detect performance bugs. 332 previously unknown performance problems are found in the latest versions of MySQL, Apache, and Mozilla applications, including 219 performance problems found by applying rules across applications.