Exploiting load testing and profiling for Performance Antipattern Detection

Trubiani, Catia; Bran, Alexander; Hoorn, André van; Avritzer, Alberto; Knoche, Holger

doi:10.1016/j.infsof.2017.11.016

Cited by 43 publications

(42 citation statements)

References 22 publications

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“…Some relevant papers related to the performance of systems were outside the scope of this study because they did not meet the inclusion and exclusion criteria established for the review procedure. In general, those papers illustrate the performance problems with synthetic examples, do not present descriptions of the identified real bugs, or these are simply either too brief or are not accompanied by the source code [13], [43]. In other cases, especially in complex systems, researchers resort to high-level diagrams to illustrate the origin of the problems that, while useful, may overlook low-level details in relation to the underlying source code [45].…”

Section: D: Challenge 4: Better Understanding Of Performance Problemsmentioning

confidence: 99%

“…For example, some papers simply point to the issue Identifier (ID) in the bug tracking system or to related papers where the bug is mentioned, or even send readers to a website that details the problem but is no longer available [9], [10]. Other papers describe the issues detected in real applications but do not show the source code [11]- [13]. This makes extremely difficult to fully under-stand or reproduce the causes that led to an underperforming system.…”

Section: Introductionmentioning

confidence: 99%

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TANDEM: A Taxonomy and a Dataset of Real-World Performance Bugs

et al. 2020

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The detection of performance bugs, like those causing an unexpected execution time, has gained much attention in the last years due to their potential impact in safety-critical and resourceconstrained applications. Much effort has been put on trying to understand the nature of performance bugs in different domains as a starting point for the development of effective testing techniques. However, the lack of a widely accepted classification scheme of performance faults and, more importantly, the lack of well-documented and understandable datasets makes it difficult to draw rigorous and verifiable conclusions widely accepted by the community. In this paper, we present TANDEM, a dual contribution related to real-world performance bugs. Firstly, we propose a taxonomy of performance bugs based on a thorough systematic review of the related literature, divided into three main categories: effects, causes and contexts of bugs. Secondly, we provide a complete collection of fully documented real-world performance bugs. Together, these contributions pave the way for the development of stronger and reproducible research results on performance testing. INDEX TERMS performance bugs, performance testing, dataset, taxonomy.

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Section: D: Challenge 4: Better Understanding Of Performance Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TANDEM: A Taxonomy and a Dataset of Real-World Performance Bugs

et al. 2020

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“…Other articles explicitly state certain performance bug patterns they attempt to detect or localize [18]- [22]. Some of these patterns are highly application dependent (e.g., [19]).…”

Section: A Performance Bugs For Evaluating and Training Detection Anmentioning

confidence: 99%

Inferring Performance Bug Patterns from Developer Commits

Chen

Winter

Suri

2019

2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE)

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Performance bugs, i.e., program source code that is unnecessarily inefficient, have received significant attention by the research community in recent years. A number of empirical studies have investigated how these bugs differ from "ordinary" bugs that cause functional deviations and several approaches to aid their detection, localization, and removal have been proposed. Many of these approaches focus on certain subclasses of performance bugs, e.g., those resulting from redundant computations or unnecessary synchronization, and the evaluation of their effectiveness is usually limited to a small number of known instances of these bugs. To provide researchers working on performance bug detection and localization techniques with a larger corpus of performance bugs to evaluate against, we conduct a study of more than 700 performance bug fixing commits across 13 popular open source projects written in C and C++ and investigate the relative frequency of bug types as well as their complexity. Our results show that many of these fixes follow a small set of bug patterns, that they are contributed by experienced developers, and that the number of lines needed to fix performance bugs is highly project dependent.

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“…Trubiani et al [20] provide a systematic process to identify performance issues with runtime data, based on load testing coming from operational profile and application profiling. From the obtained runtime data, performance antipatterns are detected, aimed at identifying common performance issues and their solutions.…”

Section: Related Workmentioning

confidence: 99%

“…Apart from technological and implementation aspects, a methodological difference distinguishes our approach from the one in [20], namely: we bring runtime data up to the design level, by annotating an UML model with the MARTE profile, and one of the main advantage of addressing performance issues at design level is to narrow down the search space for possible later empirical performance tuning.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Architecture/Runtime Model-driven Traceability for Antipattern-based Performance Improvement

Arcelli¹,

Cortellessa²,

Pompeo³

et al. 2019

EasyChair Preprints

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Model-Driven Engineering techniques may achieve a major support to the software development when they allow to manage relationships between a running system and its architectural model. These relationships can be exploited for different goals, such as the software evolution due to new functional requirements. In this paper, we define and use relationships that work as support to the performance improvement of a running system. In particular, we combine: (i) a bidirectional model transformation framework tailored to define relationships between performance monitoring data and an architectural model, with (ii) a technique for detecting performance antipatterns and for suggesting architectural changes, aimed at removing performance problems identified on the basis of runtime information. The result is an integrated approach that exploits traceability relationships between the monitoring data and the architectural model to derive recommended refactoring solutions for the system performance improvement. The approach has been applied to an e-commerce application based on microservices that has been designed by means of UML software models profiled with MARTE.

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Exploiting load testing and profiling for Performance Antipattern Detection

Cited by 43 publications

References 22 publications

TANDEM: A Taxonomy and a Dataset of Real-World Performance Bugs

TANDEM: A Taxonomy and a Dataset of Real-World Performance Bugs

Inferring Performance Bug Patterns from Developer Commits

Exploiting Architecture/Runtime Model-driven Traceability for Antipattern-based Performance Improvement

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