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A key goal of performance testing is the detection of performance degradations (i.e., regressions) compared to previous releases. Prior research has proposed the automation of such analysis through the mining of historical performance data (e.g., CPU and memory usage) from prior test runs. Nevertheless, such research has had limited adoption in practice. Working with a large industrial performance...
The backbone of cloud computing platforms like Amazon S3 and Salesforce is formed by Ultra-Large-Scale (ULS) systems, i.e., complex, globally distributed infrastructure consisting of heterogeneous sets of software and hardware nodes. To ensure that a ULS system can scale to handle increasing service demand, it is important to understand the system's performance behaviour, for example to pro-actively...
Detecting software clones in large scale projects helps improve the maintainability of large code bases. The source code representation (e.g., Java or C files) of a software system has traditionally been used for clone detection. In this paper, we propose a technique that transforms the source code to an intermediate representation, and then reuses established source-based clone detection techniques...
Performance regression testing detects performance regressions in a system under load. Such regressions refer to situations where software performance degrades compared to previous releases, although the new version behaves correctly. In current practice, performance analysts must manually analyze performance regression testing data to uncover performance regressions. This process is both time-consuming...
Business applications automate various business processes within an organization. Business analysts frequently modify business processes to maintain a competitive edge. Estimating the time needed to modify a business process is not a trivial task, since changes to the business process result in changes to the source code of which the business analyst has limited knowledge. We propose a change impact...
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