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

Abstract

Many spectrum-based fault localization (SBFL) techniques have been proposed in order to improve debugging efficiency. These SBFL techniques were designed according to different underlying assumptions and then adopt different fault locator functions to evaluate the likelihood of each statement being faulty, called suspiciousness. So far no single SBFL technique claims that it can outperform all of the others under every scenario. That is, the effectiveness of fault localization may vary considerably by just adopting a single SBFL technique. Due to the aforementioned reasons, this study presents a framework for improving fault localization effectiveness by using a Fuzzy Expert System (FES) to integrate different SBFL techniques. In the presented framework, the outputs of several SBFL techniques will be put into the fuzzification and then transferred to fuzzy input sets. After undergoing the fuzzy inference based on the given fuzzy rules, the fuzzy input sets will be transferred to a fuzzy output set. Finally, the fuzzy set will be transferred to a crisp output (called a weighted suspiciousness value). The code statements will then be ranked according to their weighted suspiciousness values. In other words, no additional instrumentations and analyses on the source code and the test suite are necessary for our approach. Our experiment results indicate that our FES-based framework is effective at combining the SBFL techniques from different equivalent groups and achieves high effectiveness on the nine subject programs. It is also noted that in the literature, most of the approaches that combine multiple SBFL techniques are learning-based and they are suitable for long-term projects with sufficient historical data. Since our approach does not reference historical data for model training, it can be applied to new software projects. Thus, the application scenarios of our approach should be complementary to those of the state-of-the-art learning-based approaches.INDEX TERMS Software debugging, fault localization, fuzzy expert system.

Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection

“…To better understand the NMI-based weight assignment over the dataset D with T instances, which comprises two parameters p and q with m, n instances, respectively, over the class label c [36]. The weights for both the features are determined through the following Equations ( 11) and (12):…”

“…ω(q) = α(q, c) mean(β(q), β(c)) (12) From Equations ( 11) and ( 12), the variable α denotes the mutual information. Furthermore, the variable β denotes the entropy.…”

“…These constraints are generally specified in the program as a conditional statement, which causes the program to stop execution if any constraint is not satisfied [11]. Program log [12] inserts are often included with the code on a hit-or-miss basis to check variables and other program information updating processes that utilize stored log files or printed run-time information to identify failure when they see strange program behavior. Using the breakpoints [13] is the other approach used in fault localization.…”

Artificial Neural Networks-Based Fault Diagnosis Model for Distribution Network

“…Existing distribution network fault identification methods can be divided into two categories. The first type is the traditional fault recognition methods, such as the Petri network [6,7], expert system [8,9], and other recognition methods based on knowledge analysis [10]. Expert systems use various reasoning techniques to analyze information about the object being diagnosed that has been gathered by computer using various criteria.…”