What is it about?
This paper describes an approach for identification of performance problems at code level. This approach consists of two parts: The performance measurement of unit tests and a root cause analysis. Since performance problems are hard to detect, it provides a measurement approach using little tests that are regularly written together with software, so-called unit tests. These are transformed into performance unit tests. In a second step, the root causes of a performance change is identified. This is done by measuring the performance of every method individually.
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Why is it important?
Performance is crucial for software usage and tends to become a problem, especially when software ages over time. By measuring the performance of unit tests and detecting their changes, performance problems can be avoided. This can help users and companies save time for waiting and operators save energy for executing slower implementations.
Perspectives
This article was a sketch of the basic ideas of my PhD thesis, which have afterwards been implemented in open source tools and published in subsequent publications.
David Georg Reichelt
Read the Original
This page is a summary of: Empirical Analysis of Performance Problems on Code Level, March 2016, ACM (Association for Computing Machinery),
DOI: 10.1145/2851553.2892038.
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