TANDEM: A Taxonomy and a Dataset of Real-World Performance Bugs

Abstract

The detection of performance bugs, like those causing an unexpected execution time, has gained much attention in the last years due to their potential impact in safety-critical and resource-constrained applications. Much effort has been put on trying to understand the nature of performance bugs in different domains as a starting point for the development of effective testing techniques. However, the lack of a widely accepted classification scheme of performance faults and, more importantly, the lack of well-documented and understandable datasets makes it difficult to draw rigorous and verifiable conclusions widely accepted by the community. In this paper, we present TANDEM, a dual contribution related to real-world performance bugs. Firstly, we propose a taxonomy of performance bugs based on a thorough systematic review of the related literature, divided into three main categories: effects, causes and contexts of bugs. Secondly, we provide a complete collection of fully documented real-world performance bugs. Together, these contributions pave the way for the development of stronger and reproducible research results on performance testing.

Keywords

• Performance bugs
• performance testing
• dataset
• taxonomy