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FOREPOST: A Tool For Detecting Performance Problems with Feedback-Driven Learning Software Testing

Author email: qluo@cs.wm.edu
Tool name: FOREPOST
Description: A goal of performance testing is to find situations when applications unexpectedly exhibit worsened characteristics for certain combinations of input values. A fundamental question of performance testing is how to select a manageable subset of the input data faster to find performance problems in applications automatically. We present a novel tool, FOREPOST, for finding performance problems in applications automatically using black-box software testing. In this paper, we demonstrate how FOREPOST extracts rules from execution traces of applications by using machine learning algorithms, and then uses these rules to select test input data automatically to steer applications towards computationally intensive paths and to find performance problems
Bibtex: "@inproceedings{luo2016forepost, title={FOREPOST: a tool for detecting performance problems with feedback-driven learning software testing}, author={Luo, Qi and Poshyvanyk, Denys and Nair, Aswathy and Grechanik, Mark}, booktitle={Proceedings of the 38th International Conference on Software Engineering Companion}, pages={593--596}, year={2016}, organization={ACM} }"
Link to public pdf: https://dl.acm.org/citation.cfm?id=2889164
Link to tool webpage: http://www.cs.wm.edu/semeru/data/ICSE16-FOREPOST/
Link to demo: Not provided by authors
Category: None
Tags: black-box testing, performance testing, machine learning
Artifacts:
Year and Conference: 2017, ICSE
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