Learning Tractable Probabilistic Models for Fault Localization

Abstract

In recent years, several probabilistic techniques have been applied to various debugging problems. However, most existing probabilistic debugging systems use relatively simple statistical models, and fail to generalize across multiple programs. In this work, we propose Tractable Fault Localization Models (TFLMs) that can be learned from data, and probabilistically infer the location of the bug. While most previous statistical debugging methods generalize over many executions of a single program, TFLMs are trained on a corpus of previously seen buggy programs, and learn to identify recurring patterns of bugs. Widely-used fault localization techniques such as TARANTULA evaluate the suspiciousness of each line in isolation; in contrast, a TFLM defines a joint probability distribution over buggy indicator variables for each line. Joint distributions with rich dependency structure are often computationally intractable; TFLMs avoid this by exploiting recent developments in tractable probabilistic models (specifically, Relational SPNs). Further, TFLMs can incorporate additional sources of information, including coverage-based features such as TARANTULA. We evaluate the fault localization performance of TFLMs that include TARANTULA scores as features in the probabilistic model. Our study shows that the learned TFLMs isolate bugs more effectively than previous statistical methods or using TARANTULA directly.

An Approach to Generate Effective Fault Localization Methods for Programs

“…To address this problem, a wide variety of Automated Fault Localization (AFL) techniques have been established in the literature to assist developers at locating the root causes of failures [23]. There are several approaches to automated fault localization such as slicing-based [27,12,22], machine-learning-based [26,30,14], and spectrum-based fault localization [19,10,6,1,28]. The Spectrum-based Fault Localization (SFL) approach has been shown to be competitive compared to the rest [16].…”

“…Zhang and Zhang[30] employed a Markov logic network to compute the suspiciousness of program statements. Nath and Domingos[14] presented a probabilistic-based fault localization technique that finds faults according to the bug patterns it learns. This technique has the capability of employing the output of spectrum-based fault localization techniques as features, and can be trained on a set of faulty programs.…”