SENov 18, 2019

Configuration-dependent Fault Localization

arXiv:1911.07906v13 citations
Originality Incremental advance
AI Analysis

This addresses a specific problem for developers of configurable systems, offering an incremental improvement over existing fault localization techniques by considering feature interactions.

The paper tackles the problem of localizing configuration-dependent bugs in configurable systems, which cause failures only in certain configurations, and proposes CoFL, a novel approach that improves baseline spectrum-based methods by increasing correctness in ranking buggy statements by over 7 times and narrowing the search space by about 15 times.

In a buggy configurable system, configuration-dependent bugs cause the failures in only certain configurations due to unexpected interactions among features. Manually localizing configuration-dependent faults in configurable systems could be highly time-consuming due to their complexity. However, the cause of configuration-dependent bugs is not considered by existing automated fault localization techniques, which are designed to localize bugs in non-configurable code. Thus, their capacity for efficient configuration-dependent localization is limited. In this work, we propose CoFL, a novel approach to localize configuration-dependent bugs by identifying and analyzing suspicious feature interactions that potentially cause the failures in buggy configurable systems. We evaluated the efficiency of CoFL in fault localization of artificial configuration-dependent faults in a highly-configurable system. We found that CoFL significantly improves the baseline spectrum-based approaches. With CoFL, on average, the correctness in ranking the buggy statements increases more than 7 times, and the search space is significantly narrowed down, about 15 times.

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