Mining of Probabilistic Controlling Behavior Model From Dynamic Software Execution Trace

He, Hongdou; Ren, Jiadong; Zhao, Guyu; Zhang, Yinghui; Hao, Xinlong

doi:10.1109/access.2019.2922998

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…In [34], a weighted software network was presented to analyze the bug propagation. 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.…”

Section: B Complex Software Networkmentioning

confidence: 99%

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%

Enhancing Spectrum-Based Fault Localization Using Fault Influence Propagation

Ren

Zhao

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…As for the defect prediction, a multi-layer structure of software network [9] was used to characterizing and mining defect patterns. By combining software network and control flow analysis, a probabilistic controlling behavior model [10] is proposed. Besides, the community structures is studied by Huang et al [11] utilizing the fault accumulation effect.…”

Section: Introductionmentioning

confidence: 99%