Locating Performance Regression Root Causes in the Field Operations of Web-based Systems: An Experience Report

Liao, Li-Zhi; Chen, Jinfu; Li, Heng; Zeng, Yi; Shang, Weiyi; Sporea, Catalin; Toma, Andrei; Sajedi, Sarah

doi:10.1109/tse.2021.3131529

Cited by 12 publications

(14 citation statements)

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“…Preventing performance issues before the commit of code changes is pursued in [23] where random forest classifiers are trained on large datasets of performance regressions. More recently, an experience report on locating the root causes of performance regressions is presented in [46] where web-access logs are exploited to tune the expected workload. However, this report targets only webbased systems, and machine learning techniques may lack to capture the relationship between problematic runtime activ-ities and their impact on system performance.…”

Section: Related Workmentioning

confidence: 99%

“…We select five Java applications that are highly concerned with their performance and have been studied in prior research [46], [61], [62]. These applications show a different complexity (in terms of number of classes) and belong to different domains, see Table 4 for their main characteristics.…”

Section: Analyzed Systemsmentioning

confidence: 99%

“…The rationale for selecting these five subject systems is that they provide evidence of all the seven performance antipatterns, as later described in the experimental results (see Section 4.4). Specifically, systems from [46] do not include ToB and EST antipattern instances. We select one system from [61] to provide evidence on the ToB antipattern.…”

Section: Analyzed Systemsmentioning

confidence: 99%

“…Inspired by [46], we build a ground truth to investigate the accuracy (along with precision, recall, and F1 score) of our detection rules. The ground truth is constituted of all the hotspot methods (i.e., over approximation of the detection outcome) since JPAD analyzes their design and performance characteristics to detect antipatterns.…”

Section: Effectiveness Of the Antipatterns' Detection Rulesmentioning

confidence: 99%

“…To answer RQ 3 , we refactor OpenMRS (i.e., the largest application among those considered in Table 4) to understand if the detection information provided by JPAD can support software engineers in solving performance issues. The selection of OpenMRS as target system for investigating the refactoring is also motivated by a recent paper [46] that analyzes the same system to locate performance regression root causes. Specifically, we focus on the OwaFilter method that JPAD detects as a Blob-Controller instance.…”

Section: Implication Of Applying Antipattern-based Refactoringsmentioning

confidence: 99%

See 4 more Smart Citations

Automated Detection of Software Performance Antipatterns in Java-Based Applications

Trubiani

Pinciroli

Biaggi

et al. 2023

IIEEE Trans. Software Eng.

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The detection of performance issues in Java-based applications is not trivial since many factors concur to poor performance, and software engineers are not sufficiently supported for this task. The goal of this manuscript is the automated detection of performance problems in running systems to guarantee that no quality-based hinders prevent their successful usage. Starting from software performance antipatterns, i.e., bad practices (e.g., extensive interaction between software methods) expressing both the problem and the solution with the purpose of identifying shortcomings and promptly fixing them, we develop a framework that automatically detects seven software antipatterns capturing a variety of performance issues in Java-based applications. Our approach is applied to real-world case studies from different domains, and it captures four real-life performance issues of Hadoop and Cassandra that were not predicted by state-of-the-art approaches. As empirical evidence, we calculate the accuracy of the proposed detection rules, we show that code commits inducing and fixing real-life performance issues present interesting variations in the number of detected antipattern instances, and solving one of the detected antipatterns improves the system performance up to 50%.

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Section: Related Workmentioning

confidence: 99%