Log4Perf

Yao, Kundi; Pádua, Guilherme B. de; Shang, Weiyi; Sporea, Steve; Toma, Andrei; Sajedi, Sarah

doi:10.1145/3184407.3184416

Cited by 25 publications

(4 citation statements)

References 7 publications

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“…LOG4PERF [7] provides placement recommendations for log statements to the specific purpose of improving application performance monitoring in web systems. It relies on log analysis and source-code instrumentation to build statistical models and to identify code blocks related to "performanceinfluencing web requests", i.e., requests with unstable performance.…”

Section: Related Workmentioning

confidence: 99%

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An Exploratory Study of Log Placement Recommendation in an Enterprise System

Cândido

Haesen²,

Aniche

et al. 2021

2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR)

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Logging is a development practice that plays an important role in the operations and monitoring of complex systems. Developers place log statements in the source code and use log data to understand how the system behaves in production. Unfortunately, anticipating where to log during development is challenging. Previous studies show the feasibility of leveraging machine learning to recommend log placement despite the data imbalance since logging is a fraction of the overall code base. However, it remains unknown how those techniques apply to an industry setting, and little is known about the effect of imbalanced data and sampling techniques. In this paper, we study the log placement problem in the code base of Adyen, a large-scale payment company. We analyze 34,526 Java files and 309,527 methods that sum up +2M SLOC. We systematically measure the effectiveness of five models based on code metrics, explore the effect of sampling techniques, understand which features models consider to be relevant for the prediction, and evaluate whether we can exploit 388,086 methods from 29 Apache projects to learn where to log in an industry setting. Our best performing model achieves 79% of balanced accuracy, 81% of precision, 60% of recall. While sampling techniques improve recall, they penalize precision at a prohibitive cost. Experiments with opensource data yield under-performing models over Adyen's test set; nevertheless, they are useful due to their low rate of false positives. Our supporting scripts and tools are available to the community.

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Section: Related Workmentioning

confidence: 99%

“…Operations and monitoring rely on metrics and data collected at different layers of the stack, from hardware, operating system, and web server logs to the application logs themselves. That data is necessary for several log analysis tasks, e.g., anomaly detection [2]- [4], root cause analysis [5], [6], and performance monitoring [7].…”

Section: Introductionmentioning

confidence: 99%

An Exploratory Study of Log Placement Recommendation in an Enterprise System

Cândido

Haesen²,

Aniche

et al. 2021

2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR)

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“…Psutil can capture detailed performance metrics and has been widely used in prior research (J. Yao et al, 2018). We collect both domain level and physical level performance metrics.…”

Section: Evaluating Performancementioning

confidence: 99%

“…Software logs are widely used in software development to track system execution behaviors Barik et al (2016). Such logs can be used to analyze system performance (T.-H. Syer et al, 2013;Yao et al, 2018). Various studies are conducted to study the use of software logs in performance analysis.…”

Section: Software Logs Analysismentioning

confidence: 99%

Performance regression detection in DevOps

Chen

2020

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings

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Performance is an important aspect of software quality. The goals of performance are typically defined by setting upper and lower bounds for response time and throughput of a system and physical level measurements such as CPU, memory, and I/O. To meet such performance goals, several performance-related activities are needed in development (Dev) and operations (Ops). Large software system failures are often due to performance issues rather than functional bugs. One of the most important performance issues is performance regression. Although performance regressions are not all bugs, they often have a direct impact on users' experience of the system. The process of detection of performance regressions in development and operations is faced with challenges. First, the detection of performance regression is conducted after the fact, i.e., after the system is built and deployed in the field or dedicated performance testing environments. Large amounts of resources are required to detect, locate, understand, and fix performance regressions at such a late stage in the development cycle. Second, even we can detect a performance regression, it is extremely hard to fix it because other changes are applied to the system after the introduction of the regression.These challenges call for further in-depth analyses of the performance regression. In this thesis, to avoid performance regression slipping into operation, we first perform an exploratory study on the source code changes that introduce performance regressions in order to understand root-causes of performance regression in the source code level. Second, we propose an approach that automatically predicts whether a test would manifest performance regressions in a code commit.Most of the performance issues are related to configurations. Therefore, third, we propose an iii approach that predicts whether a configuration option manifests a performance variation issue. To assist practitioners to analyze system performance with operational data, we propose an approach to recovering field-representative workload that can be used to detect performance regression.ivContents List of Figures xvi List of Tables xviii mance counters. Improvement are calculated by comparing with a random classifier.

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Prediction of Load Time for Different Agent Locations and Browsers

Wrzuszczak-Noga

Borzemski

2020

Advances in Intelligent Systems and Computing

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Log4Perf

Cited by 25 publications

References 7 publications

An Exploratory Study of Log Placement Recommendation in an Enterprise System

An Exploratory Study of Log Placement Recommendation in an Enterprise System

Performance regression detection in DevOps

Prediction of Load Time for Different Agent Locations and Browsers

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