Improving spectral‐based fault localization using static analysis

Abstract

Debugging is crucial for producing reliable software. One of the effective bug localization techniques is spectral-based fault localization (SBFL). It helps to locate a buggy statement by applying an evaluation metric to program spectra and ranking program components on the basis of the score it computes. SBFL is an example of a dynamic analysis -an analysis of computer program that is performed by executing it with sufficient number of test cases. Static analysis, on the other hand, is performed in a non-runtime environment. We introduce a weighting technique by combining these two kinds of program analysis. Static analysis is performed to categorize program statements into different classes and giving them weights based on the likelihood of being buggy statement. Statements are finally ranked on the basis of the weights computed by statements' categorization (static analysis) and scores computed by SBFL metrics (dynamic analysis). We evaluate the performance of our technique on Siemens test suite and Flex (having seeded bugs seeded by expert developers), Sed (having mixture of real and seeded bugs), and Space (having real bugs). In our evaluation, proposed weighting technique improves the performance of a wide variety of fault localization metrics up to 20% on single bug datasets and up to 42% on multi-bug datasets.We perform our experiments on a range of programs including Siemens test suite, Space, Sed, and Flex. Siemens test suite and Flex contain bugs seeded by expert developers, Sed have both real and seeded bugs, while Space contains real software bugs.

A Framework for Improving Fault Localization Effectiveness Based on Fuzzy Expert System

“…Similar to Xie et al's work, Wang et al [16] iteratively enlarged the non-fault region of the program based on two scenarios in order to narrow down the suspicious region, and then adopted the traditional SBFL techniques to perform fault localization. Neelofar et al [72] used static analysis to categorize program statements into various classes and then adjusted the suspiciousness scores computed by a fault locator function according to each class's weight. Similarly, Feyzi and Parsa [60] also considered the static structure and the fault-proneness of code statements, and then proposed a new approach to improve the effectiveness based on Elastic-Net regression.…”

Spectrum-based Fault Localization Techniques Application on Multiple-Fault Programs: A Review

“…Therefore, the findings show that Expense score and Exam score are the most utilized by the selected papers, respectively. [17,27,29,33,[52][53][54][55][56][57] Exam score [43, 49-51, 63, 64, 67] Wasted effort [46][47][48]65] Precision & recall [44,45,66] e) Fault Isolation Fault isolation is the process of isolating faults caused by different failures into separate clusters for efficient and more effective multiple fault localization. Most of the selected papers that utilized method such as parallel debugging used various clustering algorithms to isolate faults.…”

An evaluation of pure spectrum‐based fault localization techniques for large‐scale software systems

“…Another work by Ren and Ryder identifies methods that are likely to cause a test failure by analyzing a statically constructed call graph for the failed test and by counting the number of ancestors and descendants of methods in the graph. Finally, the approach of Neelofar et al combines SBFL with a static analysis technique that categorizes individual program statements, for example, into control statements, assignment statements, return statements, etc. Another direction is the composition of dynamic and text analysis for effective FL. For example, Le et al merged information retrieval (IR)based FL and SBFL for a more effective FL.…”