The Static Structural Complexity Metrics for Large-Scale Software System

Zhang, Hao Hua; Feng, Wen Jiang; Wu, Ling

doi:10.4028/www.scientific.net/amm.44-47.3548

Cited by 1 publication

(2 citation statements)

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“…Gu et al [40] proposed a metric of cohesion based on SN for measuring connectivity of class members. From the perspective of social network analysis (SNA), Zhang et al [41] put forward a suite of metrics for static structural complexity, which overcomes the limitations of traditional OO software metrics. Ma et al [42] proposed a hierarchical set of metrics in terms of coupling and cohesion and analyzed a sample of 12 open-source OO software systems to empirically validate the set.…”

Section: Software Networkmentioning

confidence: 99%

“…In addition to complexity metrics, software networkbased measures for stability and evolvability have also been presented by some researchers. Zhang et al [41] analyzed the evolution of software networks from several kinds of objectoriented software systems and discovered some evolution rules such as distance among nodes increase and scale-free property. Gu and Chen [44] validated software evolution laws using network measures and discussed the feasibility of modeling software evolution.…”

Section: Software Networkmentioning

confidence: 99%

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An Approach to Semantic and Structural Features Learning for Software Defect Prediction

Meilong

Xiao

et al. 2020

Mathematical Problems in Engineering

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Research on software defect prediction has achieved great success at modeling predictors. To build more accurate predictors, a number of hand-crafted features are proposed, such as static code features, process features, and social network features. Few models, however, consider the semantic and structural features of programs. Understanding the context information of source code files could explain a lot about the cause of defects in software. In this paper, we leverage representation learning for semantic and structural features generation. Specifically, we first extract token vectors of code files based on the Abstract Syntax Trees (ASTs) and then feed the token vectors into Convolutional Neural Network (CNN) to automatically learn semantic features. Meanwhile, we also construct a complex network model based on the dependencies between code files, namely, software network (SN). After that, to learn the structural features, we apply the network embedding method to the resulting SN. Finally, we build a novel software defect prediction model based on the learned semantic and structural features (SDP-S2S). We evaluated our method on 6 projects collected from public PROMISE repositories. The results suggest that the contribution of structural features extracted from software network is prominent, and when combined with semantic features, the results seem to be better. In addition, compared with the traditional hand-crafted features, the F-measure values of SDP-S2S are generally increased, with a maximum growth rate of 99.5%. We also explore the parameter sensitivity in the learning process of semantic and structural features and provide guidance for the optimization of predictors.

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Section: Software Networkmentioning

confidence: 99%

Section: Software Networkmentioning

confidence: 99%