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Content Provider | ACM Digital Library |
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Author | Yilmaz, Cemal Schmidt, Douglas C. Porter, Adam Gokhale, Aniruddha Natarajan, Balachandran Memon, Atif Krishna, Arvind S. |
Abstract | Developers of highly configurable performance-intensive software systems often use a type of in-house performance-oriented "regression testing" to ensure that their modifications have not adversely affected their software's performance across its large configuration space. Unfortunately, time and resource constraints often limit developers to in-house testing of a small number of configurations and unreliable extrapolation from these results to the entire configuration space, which allows many performance bottlenecks and sources of QoS degradation to escape detection until systems are fielded. To improve performance assessment of evolving systems across large configuration spaces, we have developed a distributed continuous quality assurance (DCQA) process called main effects screening that uses in-the-field resources to execute formally designed experiments to help reduce the configuration space, thereby allowing developers to perform more targeted in-house QA. We have evaluated this process via several feasibility studies on several large, widely-used performance-intensive software systems. Our results indicate that main effects screening can detect key sources of performance degradation in large-scale systems with significantly less effort than conventional techniques. |
Starting Page | 293 |
Ending Page | 302 |
Page Count | 10 |
File Format | |
ISBN | 1581139632 |
DOI | 10.1145/1062455.1062515 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2005-05-15 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Design of experiment theory Distributed continuous quality assurance Performance-oriented regression testing |
Content Type | Text |
Resource Type | Article |
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