Debugging often takes much effort and resources. To help developers debug, numerous information retrieval (IR)-based and spectrum-based bug localization techniques have been proposed. IR-based techniques process textual information in bug reports, while spectrum-based techniques process program spectra (i.e., a record of which program elements are executed for each test case). Both eventually generate a ranked list of program elements that are likely to contain the bug. However, these techniques only consider one source of information, either bug reports or program spectra, which is not optimal. To deal with the limitation of existing techniques, in this work, we propose a new multi-modal technique that considers both bug reports and program spectra to localize bugs. Our approach adaptively creates a bug-specific model to map a particular bug to its possible location, and introduces a novel idea of suspicious words that are highly associated to a bug. We evaluate our approach on 157 real bugs from four software systems, and compare it with a state-of-the-art IR-based bug localization method, a state-of-the-art spectrum-based bug localization method, and three state-of-the-art multi-modal feature location methods that are adapted for bug localization. Experiments show that our approach can outperform the baselines by at least 47.62%, 31.48%, 27.78%, and 28.80% in terms of number of bugs successfully localized when a developer inspects 1, 5, and 10 program elements (i.e., Top 1, Top 5, and Top 10), and Mean Average Precision (MAP) respectively.

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Numerous rule-based specification mining approaches have been proposed in the literature. Many of these approaches analyze a set of execution traces to discover interesting usage rules, e.g., whenever lock() is invoked, eventually unlock() is invoked. These techniques often generate and enumerate a set of candidate rules and compute some interestingness scores. Rules whose interestingness scores are above a certain threshold would then be output. In past studies, two measures, namely support and confidence, which are well-known measures, are often used to compute these scores. However, aside from these two, many other interestingness measures have been proposed. It is thus unclear if support and confidence are the best interestingness measures for specification mining. In this work, we perform an empirical study that investigates the utility of 38 interestingness measures in recovering correct specifications of classes from Java libraries. We used a ground truth dataset consisting of 683 rules and recorded execution traces that are produced when we run the DaCapo test suite. We apply 38 different interestingness measures to identify correct rules from a pool of candidate rules. Our study highlights that many measures are on par to support and confidence. Some of the measures are even better than support or confidence and at least one of the measures is statistically significantly better than the two measures. We also find that compositions of several measures with support statistically significantly outperform the composition of support and confidence. Our findings highlight the need to look beyond standard support and confidence to find interesting rules.

Abstract-Spectrum-based fault localization refers to the process of identifying program units that are buggy from two sets of execution traces: normal traces and faulty traces. These approaches use statistical formulas to measure the suspiciousness of program units based on the execution traces. There have been many spectrum-based fault localization approaches proposing various formulas in the literature. Two of the best performing and well-known ones are Tarantula and Ochiai. Recently, Xie et al. [18] find that theoretically, under certain assumptions, two families of spectrum-based fault localization formulas outperform all other formulas including those of Tarantula and Ochiai. In this work, we empirically validate Xie et al.'s findings by comparing the performance of the theoretically best formulas against popular approaches on a dataset containing 199 buggy versions of 10 programs. Our empirical study finds that Ochiai and Tarantula statistically significantly outperforms 3 out of 5 theoretically best fault localization techniques. For the remaining two, Ochiai also outperforms them, albeit not statistically significantly. This happens because an assumption in Xie et al.'s work is not satisfied in many fault localization settings.