Generating range fixes for software configuration

Xiong, Yingfei; Hubaux,; Czarnecki,

doi:10.1109/icse.2012.6227206

Cited by 51 publications

(61 citation statements)

References 29 publications

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“…We plan to investigate alternative strategies in generating misconfigurations. One possible direction is to employ advanced test generation techniques [26,59] to guide the creation of misconfigurations.…”

Section: Discussionmentioning

confidence: 99%

“…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: Discussionmentioning

confidence: 99%

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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“…Automated and interactive configurators use such models to support users in navigating a complex configuration space [8], [22], [61], [62]. However, many systems have no documented variability model or rely on informal textual descriptions of constraints (e.g., the FreeBSD kernel [48]).…”

Section: Introductionmentioning

confidence: 99%

Where Do Configuration Constraints Stem From? An Extraction Approach and an Empirical Study

Nadi

Berger

Kästner

et al. 2015

IIEEE Trans. Software Eng.

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Abstract-Highly configurable systems allow users to tailor software to specific needs. Valid combinations of configuration options are often restricted by intricate constraints. Describing options and constraints in a variability model allows reasoning about the supported configurations. To automate creating and verifying such models, we need to identify the origin of such constraints. We propose a static analysis approach, based on two rules, to extract configuration constraints from code. We apply it on four highly configurable systems to evaluate the accuracy of our approach and to determine which constraints are recoverable from the code. We find that our approach is highly accurate (93 % and 77 % respectively) and that we can recover 28 % of existing constraints. We complement our approach with a qualitative study to identify constraint sources, triangulating results from our automatic extraction, manual inspections, and interviews with 27 developers. We find that, apart from low-level implementation dependencies, configuration constraints enforce correct runtime behavior, improve users' configuration experience, and prevent corner cases. While the majority of constraints is extractable from code, our results indicate that creating a complete model requires further substantial domain knowledge and testing. Our results aim at supporting researchers and practitioners working on variability model engineering, evolution, and verification techniques.

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“…Automated and interactive configurators use such models to support users in navigating a complex configuration space [9,21,53,54]. However, many systems have no documented variability model or rely on informal textual descriptions of constraints (e.g., the FreeBSD kernel [42]).…”

Section: Introductionmentioning

confidence: 99%

Mining configuration constraints: static analyses and empirical results

Nadi

Berger

Kästner

et al. 2014

Proceedings of the 36th International Conference on Software Engineering

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109

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Highly-configurable systems allow users to tailor the software to their specific needs. Not all combinations of configuration options are valid though, and constraints arise for technical or non-technical reasons. Explicitly describing these constraints in a variability model allows reasoning about the supported configurations. To automate creating variability models, we need to identify the origin of such configuration constraints. We propose an approach which uses buildtime errors and a novel feature-effect heuristic to automatically extract configuration constraints from C code. We conduct an empirical study on four highly-configurable open-source systems with existing variability models having three objectives in mind: evaluate the accuracy of our approach, determine the recoverability of existing variability-model constraints using our analysis, and classify the sources of variability-model constraints. We find that both our extraction heuristics are highly accurate (93 % and 77 % respectively), and that we can recover 19 % of the existing variability-models using our approach. However, we find that many of the remaining constraints require expert knowledge or more expensive analyses. We argue that our approach, tooling, and experimental results support researchers and practitioners working on variability model re-engineering, evolution, and consistency-checking techniques.

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Generating range fixes for software configuration

Cited by 51 publications

References 29 publications

Proactive detection of inadequate diagnostic messages for software configuration errors

Proactive detection of inadequate diagnostic messages for software configuration errors

Where Do Configuration Constraints Stem From? An Extraction Approach and an Empirical Study

Mining configuration constraints: static analyses and empirical results

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