Automated diagnosis of software configuration errors

Zhang, Sai; Ernst, M.

doi:10.1109/icse.2013.6606577

Cited by 76 publications

(52 citation statements)

References 27 publications

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“…Many techniques have been developed to troubleshoot anomalies caused by configuration errors [4,61,65], diagnose certain types of configuration errors [2,3], automate configuration tasks [30,66,67], and suggest fixes for a configuration error [49,59], but none of them helps developers identify inadequate diagnostic messages for software configuration errors. While previous research has mitigated the impact of configuration errors, the best way to help users troubleshoot a misconfiguration is for the software to issue a helpful error report.…”

Section: Comparison With Existing Techniquesmentioning

confidence: 99%

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Proactive detection of inadequate diagnostic messages for software configuration errors

Zhang

Ernst

2015

Proceedings of the 2015 International Symposium on Software Testing and Analysis

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This paper presents a technique to detect inadequate (i.e., missing or ambiguous) diagnostic messages for configuration errors issued by a configurable software system.The technique injects configuration errors into the software under test, monitors the software outcomes under the injected configuration errors, and uses natural language processing to analyze the output diagnostic message caused by each configuration error. The technique reports diagnostic messages that may be unhelpful in diagnosing a configuration error.We implemented the technique for Java in a tool, ConfDiagDetector. In an evaluation on 4 real-world, mature configurable systems, ConfDiagDetector reported 43 distinct inadequate diagnostic messages (25 missing and 18 ambiguous). 30 of the detected messages have been confirmed by their developers, and 12 more have been identified as inadequate by users in a user study. On average, ConfDiagDetector required 5 minutes of programmer time and 3 minutes of compute time to detect each inadequate diagnostic message.

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Section: Comparison With Existing Techniquesmentioning

confidence: 99%

“…Most existing techniques assist developers in diagnosing an exhibited configuration error [3,49,66,67]. By contrast, ConfDiagDetector proactively detects inadequate diagnostic messages before a configuration error actually arises in the field.…”

Section: Comparison With Existing Techniquesmentioning

confidence: 99%

Proactive detection of inadequate diagnostic messages for software configuration errors

Zhang

Ernst

2015

Proceedings of the 2015 International Symposium on Software Testing and Analysis

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“…There have been a number of studies on configuration management [9], [19], [11], [12], [36], [37]. Yin et al perform an empirical study on configuration issues in one commercial and 4 open source software systems [9].…”

Section: Related Workmentioning

confidence: 99%

“…Recently, much research effort has been made to detect, diagnose, and fix configuration bugs [10], [11], [12]. Wang et al propose PeerPressure which identifies configuration errors by leveraging statistical analysis [10].…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al propose PeerPressure which identifies configuration errors by leveraging statistical analysis [10]. Zhang and Ernst develop a tool named ConfDiagnoser which combines static analysis, dynamic profiling, and statistical analysis to identify the root cause of a configuration bug [11]. Xu et al propose SPEX to automatically infer configuration constraints from source code, and then use these constraints to expose misconfiguration vulnerabilities, and detect error-prone configuration design [12].…”

Section: Introductionmentioning

confidence: 99%

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Automated Configuration Bug Report Prediction Using Text Mining

Xia

Qiu

et al. 2014

2014 IEEE 38th Annual Computer Software and Applications Conference

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Abstract-Configuration bugs are one of the dominant causes of software failures. Previous studies show that a configuration bug could cause huge financial losses in a software system. The importance of configuration bugs has attracted various research studies, e.g., to detect, diagnose, and fix configuration bugs. Given a bug report, an approach that can identify whether the bug is a configuration bug could help developers reduce debugging effort. We refer to this problem as configuration bug reports prediction. To address this problem, we develop a new automated framework that applies text mining technologies on the naturallanguage description of bug reports to train a statistical model on historical bug reports with known labels (i.e., configuration or non-configuration), and the statistical model is then used to predict a label for a new bug report. Developers could apply our model to automatically predict labels of bug reports to improve their productivity. Our tool first applies feature selection techniques (e.g., information gain and Chi-square) to preprocess the textual information in bug reports, and then applies various text mining techniques (e.g., naive Bayes, SVM, naive Bayes multinomial) to build statistical models. We evaluate our solution on 5 bug report datasets including accumulo, activemq, camel, flume, and wicket. We show that naive Bayes multinomial with information gain achieves the best performance. On average across the 5 projects, its accuracy, configuration F-measure and non-configuration F-measure are 0.811, 0.450, and 0.880, respectively. We also compare our solution with the method proposed by Arshad et al.. The results show that our proposed approach that uses naive Bayes multinomial with information gain on average improves accuracy, configuration F-measure and non-configuration F-measure scores of Arshad et al.'s method by 8.34%, 103.7%, and 4.24%, respectively.

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Predicting Hadoop misconfigurations using machine learning

et al. 2020

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Distributed applications are popular for heavy workloads where the resources of a single machine are not sufficient. These distributed applications come with many parameters to tune so that cluster resources can be effectively utilized.However, any misconfiguration of the available parameters may result in suboptimal performance of one or more machines in the cluster. These events may go unnoticed or can result in crashes. This problem of misconfigured parameters has no straightforward solution due to the variety of parameters and vastly different workloads being processed. In this article, we propose a methodology for machine learning-based detection of misconfigurations. We collect data mined from system resource utilization, Hadoop logs, and job-level metrics to train a model using decision tree and support vector machine. The models are used to identify whether a set of configuration parameters could result in a crash or a slowdown for a specific workload. The approach explained in this article can be extended to other distributed big data applications, such as Spark, Hive, Pig, and so on. K E Y W O R D Sbig data, Hadoop, machine learning, misconfiguration, prediction 1 wileyonlinelibrary.com/journal/spe Softw: Pract Exper. 2020;50:1168-1183.

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Automated diagnosis of software configuration errors

Cited by 76 publications

References 27 publications

Proactive detection of inadequate diagnostic messages for software configuration errors

Proactive detection of inadequate diagnostic messages for software configuration errors

Automated Configuration Bug Report Prediction Using Text Mining

Predicting Hadoop misconfigurations using machine learning

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