Detection of semantically similar code

Wang, Tiantian; Wang, Kechao; Su, Xin; Ma, Peijun

doi:10.1007/s11704-014-3430-1

Cited by 14 publications

(10 citation statements)

References 26 publications

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“…This method identifies the difference of execution states and structural semantics [42] between the defective program and the sample program, and locates suspicious statements even in the absence of successful test cases. It focus on solving the problem of code variation and failure propagation.…”

Section: Research Frameworkmentioning

confidence: 99%

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Fault localization by analyzing failure propagation with samples in cloud computing environment

Wang

2020

J Cloud Comp

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With the development of information technology such as cloud computing, IoT, etc, software becomes the infrastructure. On the one hand, it is critical to ensure the reliability of software, on the other, sample code can be mined from open source software to provide reference for automatic debugging. Most of existing automated debugging researches are based on the assumption that defect programs are nearly correct, therefore they can successfully pass some test cases and fail to execute others. However, this assumption sometimes does not hold. In view of the fact that a programs may fail for all the given test cases in the test suite, but there are a large number of examples available for reference, a sample based fault localization method is studied. A fault localization method by analyzing the context of failure propagation is proposed, which locates suspicious statements by identifying the execution status and structural semantics differences between the defective program and sample program. Through the interactive analysis of value sequences and structure semantics, the influence of code variations and failure propagation is reduced. The experimental results have shown that the method can effectively locate suspicious statements and provide assistance for defect repair when there are enough sample programs.

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Section: Research Frameworkmentioning

confidence: 99%

“…Then, in order to reduce the false positives of suspicious statements caused by failure propagation. The structural semantics of the programs is further analyzed based on control dependence trees [42], to identify possible matching variable pairs and statements.…”

Section: Research Frameworkmentioning

confidence: 99%

Fault localization by analyzing failure propagation with samples in cloud computing environment

Wang

2020

J Cloud Comp

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“…E is the set of edges representing data or control dependence relationship between nodes [10].  DEFINITION 3 (Control Dependence Graph).…”

Section: Program Dependence Graphmentioning

confidence: 99%

“…CMGA [10] is also a graph-based tool that detects semantic similar code by combining software metrics and program dependence graphs. First, CMGA generates the augmented system dependence graph and transforms the control dependence graph into the control dependence tree.…”

Section: E Efficiency Of Csdmentioning

confidence: 99%

“…Generally those techniques can be divided into text-based [1] [2], token-based [3], tree-based [4], graph-based [5][6] [7] and metrics-based [8]. Recent research shows that in order to improve the accuracy of detection, semantics-based [9] [10[11] get a closer study, and a number of mixed use of technology [10][12] [13] to improve the efficiency of detection. The method based on program dependence graph has a very good detection effect in semantic level, but there are two problems need to be solved.…”

Section: Introductionmentioning

confidence: 99%

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Code Similarity Detection by Program Dependence Graph

Zhang¹,

Yan²,

Zhang³

2016

Proceedings of the 2016 International Conference on Computer Engineering and Information Systems

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Abstract-Existing tools are rarely able to check semantic similarity code. Although some tools consider the programs' semantics, but their detection efficiency is not high. At the same time, function splitting problem confused graph-based tools. In this paper, We propose a approach which combines the software static analysis with the program dependence graph to detect similar code and solve function splitting problem. First, in order to reduce the computational complexity of the program dependence graph's building and comparison, we used software static analysis method to extract testing functions fair. Then, we create the program dependence graph for testing functions fair. Finally, graph matching is performed on isomorphism testing to output similar function and the set of functions which may be split or merged. Experiment results show that our approach can detect all kinds of variations in the code, and adding software static analysis can improve the the detection efficiency based on program dependence graph approach.

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Software smell detection techniques: A systematic literature review

AbuHassan

Alshayeb

Ghouti

2020

J Software Evolu Process

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Software smells indicate design or code issues that might degrade the evolution and maintenance of software systems. Detecting and identifying these issues are challenging tasks. This paper explores, identifies, and analyzes the existing software smell detection techniques at design and code levels. We carried out a systematic literature review (SLR) to identify and collect 145 primary studies related to smell detection in software design and code. Based on these studies, we address several questions related to the analysis of the existing smell detection techniques in terms of abstraction level (design or code), targeted smells, used metrics, implementation, and validation. Our analysis identified several detection techniques categories. We observed that 57% of the studies did not use any performance measures, 41% of them omitted details on the targeted programing language, and the detection techniques were not validated in 14% of these studies. With respect to the abstraction level, only 18% of the studies addressed bad smell detection at the design level. This low coverage urges for more focus on bad smell detection at the design level to handle them at early stages. Finally, our SLR brings to the attention of the research community several opportunities for future research.

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Detection of semantically similar code

Cited by 14 publications

References 26 publications

Fault localization by analyzing failure propagation with samples in cloud computing environment

Fault localization by analyzing failure propagation with samples in cloud computing environment

Code Similarity Detection by Program Dependence Graph

Software smell detection techniques: A systematic literature review

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