A family of generalized entropies and its application to software fault localization

Many spectrum-based fault localization techniques have been proposed to measure how likely each program element is the root cause of a program failure. For various bugs, the best technique to localize the bugs may differ due to the characteristics of the buggy programs and their program spectra. In this paper, we leverage the diversity of existing spectrum-based fault localization techniques to better localize bugs using data fusion methods. Our proposed approach consists of three steps: score normalization, technique selection, and data fusion. We investigate two score normalization methods, two technique selection methods, and five data fusion methods resulting in twenty variants of Fusion Localizer. Our approach is bug specific in which the set of techniques to be fused are adaptively selected for each buggy program based on its spectra. Also, it requires no training data, i.e., execution traces of the past buggy programs.We evaluate our approach on a common benchmark dataset and a dataset consisting of real bugs from three medium to large programs. Our evaluation demonstrates that our approach can significantly improve the effectiveness of existing state-of-the-art fault localization techniques. Compared to these state-of-the-art techniques, the best variants of Fusion Localizer can statistically significantly reduce the amount of code to be inspected to find all bugs. Our best variants can increase the proportion of bugs localized when developers only inspect the top 10% most suspicious program elements by more than 10% and increase the number of bugs that can be successfully localized when developers only inspect up to 10 program blocks by more than 20%.

show abstract

“…Thus, the weight assigned to Ochiai is 1-1. 33 2 =0.335. Similarly, the weights for Klosgen and Piatetsky Shapiro are 0.335 and 0.67.…”

Section: 1) Corramentioning

confidence: 99%

Fusion fault localizers

Lucia

Xia

2014

Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

View full text Add to dashboard Cite

show abstract

“…In [10], authors proposed a family of generalized entropies which is defined using the following functional:…”

Section: Preliminaries Of Information Theorymentioning

confidence: 99%

“…Table 4 gives the number of faulty versions and test cases of each program and the size in terms of lines of code. The seven programs of the Siemens suite have been employed by many fault localization studies [1][2][3][4][5][6][7][8][9][10][11]. The correct versions, 132 faulty versions of the programs and all the test cases are downloaded from The Siemens Suite [45].…”

Section: Empirical Studymentioning

confidence: 99%

Inforence: effective fault localization based on information-theoretic analysis and statistical causal inference

Feyzi

Parsa

2019

Front. Comput. Sci.

View full text Add to dashboard Cite

In this paper, a novel approach, Inforence, is proposed to isolate the suspicious codes that likely contain faults. Inforence employs a feature selection method, based on mutual information, to identify those bug-related statements that may cause the program to fail. Because the majority of a program faults may be revealed as undesired joint effect of the program statements on each other and on program termination state, unlike the state-of-the-art methods, Inforence tries to identify and select groups of interdependent statements which altogether may affect the program failure. The interdependence amongst the statements is measured according to their mutual effect on each other and on the program termination state. To provide the context of failure, the selected bug-related statements are chained to each other, considering the program static structure. Eventually, the resultant cause-effect chains are ranked according to their combined causal effect on program failure. To validate Inforence, the results of our experiments with seven sets of programs include Siemens suite, gzip, grep, sed, space, make and bash are presented. The experimental results are then compared with those provided by different fault localization techniques for the both single-fault and multi-fault programs. The experimental results prove the outperformance of the proposed method compared to the state-of-the-art techniques.research focus is on developing statistical algorithms to improve software quality with an emphasis on statistical fault localization and automated test data generation.

show abstract

“…Fault Localization [3, 7, 12, 22, 33, 42, 48, 54-56, 71, 72] aims to precisely diagnose potential buggy locations to facilitate manual bug fixing. The most widely studied spectrum-based fault localization (SBFL) techniques usually apply statistical analysis (e.g., Tarantula [22], Ochiai [3], and Ample [12]) or learning techniques [7,[54][55][56] to the execution traces of both passed and failed tests to identify the most suspicious code elements (e.g., statements/methods). The insight is that code elements primarily executed by failed tests are more suspicious than the elements primarily executed by passed tests.…”

Section: Background and Related Workmentioning

confidence: 99%

Can Automated Program Repair Refine Fault Localization?

Lou,

Ghanbari,

et al. 2019

Preprint

View full text Add to dashboard Cite

Software bugs are prevalent in modern software systems and notoriously hard to debug manually. Therefore, a large body of research efforts have been dedicated to automated software debugging, including both automated fault localization and program repair. However, the existing fault localization techniques are usually ineffective on real-world software systems while even the most advanced program repair techniques can only fix a small ratio of real-world bugs. Although fault localization and program repair are inherently connected, we observe that in the literature their only connection is that program repair techniques usually use off-the-shelf fault localization techniques (e.g., Ochiai) to determine the potential candidate statements/elements for patching. In this work, we explore their connection in the other direction, i.e., can program repair in turn help with fault localization? In this way, we not only open a new dimension for more powerful fault localization, but also extend the application scope of program repair to all possible bugs (not only the bugs that can be directly automatically fixed). We have designed ProFL, a simplistic approach using patch-execution results (from program repair) as the feedback information for fault localization. The experimental results on the widely used Defects4J benchmark show that the basic ProFL can already localize 161 of the 395 studied bugs within Top-1, while state-of-the-art spectrum and mutation based fault localization techniques at most localize 117 within Top-1. We also demonstrate ProFL's effectiveness under different settings. Lastly, we show that ProFL can further boost state-of-the-art fault localization via both unsupervised and supervised learning. * This work was mainly done when Yiling Lou was a visiting student in UT Dallas.

show abstract

A family of generalized entropies and its application to software fault localization

Cited by 9 publications

References 20 publications

Fusion fault localizers

Fusion fault localizers

Inforence: effective fault localization based on information-theoretic analysis and statistical causal inference

Can Automated Program Repair Refine Fault Localization?

Contact Info

Product

Resources

About