Simultaneous Localization of Software Faults Based on Complex Network Theory

Zakari, Abubakar; Lee, Sai Peck; Chong, Chun Yong

doi:10.1109/access.2018.2829541

Cited by 31 publications

(32 citation statements)

References 50 publications

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“…To study the inner control relations of software, in our previous work [35], an approach of mining dynamic software semantic network was proposed based on the control-flow analysis. In the field of software fault localization, Zakari et al [3] constructed an interactive network at the statement-level granularity and proposed the fault localization approach based on degree and betweenness centralities. At present, the research of fault localization using the complex network is very few.…”

Section: B Complex Software Networkmentioning

confidence: 99%

“…At present, the research of fault localization using the complex network is very few. In [3], although the complex theory is used for fault localization, the software hit-spectrum is abandoned. In our paper, the complex software network and software hit-spectrum are combined, which establishes the foundation of fault influence propagation approach.…”

Section: B Complex Software Networkmentioning

confidence: 99%

“…In summary, our proposed approach of enhancing fault localization using PageRank based on complex network theory comprises the following contents: (1) tracing software execution traces and extracting software test coverage and method call relations; (2) constructing the Fault Influence Network to characterize fault suspiciousness and fault influence propagation of methods; (3) proposing PageRank-based Suspiciousness to enhance the performance of fault locators.…”

Section: Pagerank Algorithmmentioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Porfirio Tramontana . the expensive cost of software maintenance [3]. Meanwhile, with the increasing complexity of software systems, locating software failures accurately in millions of lines of code is extremely difficult.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Spectrum-Based Fault Localization Using Fault Influence Propagation

Ren

Zhao

et al. 2020

IEEE Access

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With the rapidly increasing complexity and indispensable status of software systems, unprecedented challenges have been brought to software debugging and fault repair. Among the states of art automated fault localization techniques, spectrum-based fault localization (SBFL) is one of the most widely studied heuristic approaches. While, existing SBFL techniques are mostly focused on the analysis of the test spectrum, which loses sight of the effectiveness implied in interactions of software entities. In this paper, an optimized fault localization approach is proposed, which integrates spectrum-based fault localization and fault influence propagation analysis. The intuition is that the entity associated with more suspicious entities is more likely to be faulty. (1) A dynamic-instrumenting execution tracer is developed to record test coverage data and method call relations simultaneously. (2) The Fault Influence Network (FIN) based on complex network theory is constructed, of which the network topology is abstracted from method call relations and the weights of nodes are calculated applying a raw fault locator. Thus, the test spectrum and the method interactive relations are combined for further collaborative analysis. (3) A PageRank-based fault localization approach is proposed. By the computation of fault influence propagation, the suspiciousness score PR−Susp of the real faulty method can be enhanced significantly. Also, a candidate set pruning based on failing tests is implemented, which is used for the narrowing of investigation. Experiments are conducted over real-world fault dataset Defects4J with 33 raw spectrum-based fault locators, which proves that the proposed approach improves the baseline 14.9% averagely on the metric acc@5 with a growth rate of over 39% on all metrics of acc@1, acc@3, acc@5, MAP, and MWE.

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Section: B Complex Software Networkmentioning

confidence: 99%

Section: B Complex Software Networkmentioning

confidence: 99%

Section: Pagerank Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing Spectrum-Based Fault Localization Using Fault Influence Propagation

Ren

Zhao

et al. 2020

IEEE Access

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“…Association rule is the probability of occurrence of Y in the case of occurrence of X, which is a conclusion summarized from a large amount of data and widely applied in real life. For example, many aspects such as the display of supermarket items, the promotion of financial products, news recommendation, warehouse planning, and the analysis of network faults [9] are of high practical value [10].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization-Based Association Rule Mining in Big Data Environment

Zhou

2019

IEEE Access

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With the explosive growth of information data in today's society, the continuous accumulation and increase of data in recent years make it difficult to extract useful information from it, so data mining comes into being. Association rule mining is an important part of data mining technology. Association rule mining is the discovery of frequent item sets in a large amount of data and the mining of strong association relations between them. Traditional association rule algorithms need to set minimum support and minimum confidence in advance. However, these two values are largely influenced by human subjectivity. Many scholars use average and weight to set these two values, but the effect is still not very good. In order to solve this problem, this paper proposed an improved algorithm of association rules-PSOFP growth algorithm, this algorithm is introduced into intelligent algorithm, particle swarm optimization algorithm, it can find the global optimal solution, we use this fact to find the optimal support, then using FP-growth algorithm for mining association rules, and finally put forward by information entropy to measure effectiveness in association rules mining, and the improved algorithm was applied to the social security event correlation analysis, the improved algorithm proved to our expectations.

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Combining object‐oriented metrics and centrality measures to predict faults in object‐oriented software: An empirical validation

Ouellet

Badri

2023

J Software Evolu Process

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Many object‐oriented metrics have been proposed in the literature to measure various structural properties of object‐oriented software. Furthermore, many centrality measures have been introduced to identify central nodes in large networks. However, few studies have used them to measure dependencies in software systems. In fact, centrality measures, as opposed to most traditional object‐oriented metrics that mainly focus on intrinsic properties of classes, can be used to better model the control flow and to identify the most important classes in a software system. This paper aims (1) to investigate the relationships between object‐oriented metrics and centrality measures and (2) to explore the ability of their combination to support fault‐proneness prediction from different perspectives (fault‐prone classes, fault severity, and number of faults). Many studies in the literature have addressed the prediction of fault‐prone classes, from different perspectives, using object‐oriented metrics. The main motivation here is in fact to investigate if the information captured by centrality measures is related to fault proneness and complementary to the information captured by object‐oriented metrics and to investigate if the combination of object‐oriented metrics and centrality measures improves the performance of fault‐proneness prediction significantly. We used size, complexity, and coupling object‐oriented metrics in addition to various centrality measures. We collected data from 20 different versions of five open‐source Java software systems. We first studied the relationships between selected metrics and their relationships to fault proneness. Then, we built different models to predict fault‐prone classes using several machine learning algorithms. In addition, we built models to predict if a class contains a high severity fault, and the number of faults in a class. Results indicate that using centrality measures in combination with object‐oriented metrics improves the prediction of fault‐prone classes as well as the prediction of the number of faults in a class. However, the combination has no significant impact, according to the data we collected, on the quality of the prediction of fault severity. Moreover, using centrality measures in combination with object‐oriented metrics also improves the prediction performance of fault proneness and the number of faults in both cross‐version and cross‐system validation.

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Simultaneous Localization of Software Faults Based on Complex Network Theory

Cited by 31 publications

References 50 publications

Enhancing Spectrum-Based Fault Localization Using Fault Influence Propagation

Enhancing Spectrum-Based Fault Localization Using Fault Influence Propagation

Particle Swarm Optimization-Based Association Rule Mining in Big Data Environment

Combining object‐oriented metrics and centrality measures to predict faults in object‐oriented software: An empirical validation

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