Predicting Fault-prone Software Module Using Data Mining Technique and Fuzzy Logic

Pandey, Ajeet Kumar; Goyal, Neeraj Kumar

doi:10.47893/ijcct.2012.1105

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…The concept of code complexity is not an atomic concept. AK Pandey (2010) (HC) to classify the software module as fault prone or not. Regarding the previous studies (Pandey, 2010;Zhou et al, 2010), LOC, CM and HC are indeed better fault-prone predictors than other complexity metrics.…”

Section: Local Bestmentioning

confidence: 99%

“…AK Pandey (2010) (HC) to classify the software module as fault prone or not. Regarding the previous studies (Pandey, 2010;Zhou et al, 2010), LOC, CM and HC are indeed better fault-prone predictors than other complexity metrics. In the proposed method, the combination of LOC, CM and HC is used to measure the complexity of a program source code; after measuring the complexity weight of each blocks using proposed equations the most fault-prone paths of the program are identified by the proposed FA.…”

Section: Local Bestmentioning

confidence: 99%

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Mutation reduction in software mutation testing using firefly optimization algorithm

Shomali

Arasteh

2020

DTA

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PurposeFor delivering high-quality software applications, proper testing is required. A software test will function successfully if it can find more software faults. The traditional method of assessing the quality and effectiveness of a test suite is mutation testing. One of the main drawbacks of mutation testing is its computational cost. The research problem of this study is the high computational cost of the mutation test. Reducing the time and cost of the mutation test is the main goal of this study.Design/methodology/approachWith regard to the 80–20 rule, 80% of the faults are found in 20% of the fault-prone code of a program. The proposed method statically analyzes the source code of the program to identify the fault-prone locations of the program. Identifying the fault-prone (complex) paths of a program is an NP-hard problem. In the proposed method, a firefly optimization algorithm is used for identifying the most fault-prone paths of a program; then, the mutation operators are injected only on the identified fault-prone instructions.FindingsThe source codes of five traditional benchmark programs were used for evaluating the effectiveness of the proposed method to reduce the mutant number. The proposed method was implemented in Matlab. The mutation injection operations were carried out by MuJava, and the output was investigated. The results confirm that the proposed method considerably reduces the number of mutants, and consequently, the cost of software mutation-test.Originality/valueThe proposed method avoids the mutation of nonfault-prone (simple) codes of the program, and consequently, the number of mutants considerably is reduced. In a program with n branch instructions (if instruction), there are 2n execution paths (test paths) that the data and codes into each of these paths can be considered as a target of mutation. Identifying the error-prone (complex) paths of a program is an NP-hard problem. In the proposed method, a firefly optimization algorithm as a heuristic algorithm is used for identifying the most error-prone paths of a program; then, the mutation operators (faults) are injected only on the identified fault-prone instructions.

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Section: Local Bestmentioning

confidence: 99%

Section: Local Bestmentioning

confidence: 99%

Mutation reduction in software mutation testing using firefly optimization algorithm

Shomali

Arasteh

2020

DTA

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“…Measurements help differ slightly with selection of programming language, nevertheless most usually caught ones are-techniques per class, percent branch statements, LOC, maximum method complexity, classes & interfaces, & percent lines with the comments. Principally a JHawk, java metric tool [13] need advanced from a stand-alone GUI provision to incorporate an order transport form and an eclipse plug in. It compromises to process IDE coordination (for Visual period for Java) Also gives the HTML, XML, and CSV send out formats.…”

Section: Source Code Monitor (Sm)mentioning

confidence: 99%

An efficient Software Source Code Metrics for Implementing for Software quality analysis

Srivastava¹

2019

IJETER

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The challenging task is evaluating size of a complex and enormous software framework. Primarily in life cycle of project, while necessities for framework might be functional & immature described only at extreme level, profiles of resource are require for suitable staffing, funding, and progress of feasible project strategy. Same project historical software size information& trends gives a device to calculate size of software, making a possible evaluation method. As processors are being utilized in each and every imaginable region in current world, software quality gets a main feature in planned achievement of a human & business safety in common. Discovering quality factors of software & illustration those into computable measures will be an essential feature in viable achievement of end product. Program features illustration into these values of metrics depicts information framework behavior & structural complexity. In this survey, 5 software metric are utilized, they are lines of code (LOC), lines of comment (COM), cyclomatic complexity (MVG), number of modules (NOM), and Halstead volume (HV) have been used to examine a group of 3 sorting programs of java. The 3 software measurement devices have been applied on them to confirm their presentation w.r.to metrics cited there also a resultant metric maintainability index has been measured from basic metrics to designate comparative maintainability of the source code. The comparative study of selected devices has been undertaken to expose how they change in providing outcomes for similar programs. Additionally, few other quality features that might be resultant from essential metrics are cited in next sub section.

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“…We use BayesNet in our experiment because it is robust to overfitting and does not assume data independence. As a matter of fact, many machine learning techniques such as neural networks[7],[37],[67], decision trees[6],[27],[28], case-based reasoning[36],[38],[55], Naïve Bayes[15],[31],[44], fuzzy logic[56], logistic regression[5],[9],[16], SVM[20],[25],[26], random forests[39],[63], and so on have been used for predicting software fault-proneness in the past. We want to emphasize that the focus of this study is to evaluate prediction effectiveness of the metrics derived from newly designed social networks.…”

mentioning

confidence: 99%

Using Tri-Relation Networks for Effective Software Fault-Proneness Prediction

Wong

Lee

et al. 2019

IEEE Access

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Software modules and developers are two core elements during the process of software development. Previous studies have shown that analyzing relations between 1) software modules; (2) developers; and (3) modules and developers, is critical to understand how they interact with each other, which ultimately affects software quality. Specifically, relations such as developer contribution relation, module dependency relation, and developer collaboration relation have been used independently or in pairs to build networks for software fault-proneness prediction. However, none of them investigate the joint effort of these three relations. Bearing this in mind, in this paper, we propose a tri-relation network, a weighted network that integrates developer contribution, module dependency, and developer collaboration relations to study their combined impact on software quality. Four network node centrality metrics are further derived from the proposed network to predict the fault-proneness of a given software module at the file level. Moreover, we have explored a mechanism to refine current networks in order to further improve the effectiveness of software fault-proneness prediction.

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Predicting Fault-prone Software Module Using Data Mining Technique and Fuzzy Logic

Cited by 8 publications

References 28 publications

Mutation reduction in software mutation testing using firefly optimization algorithm

Mutation reduction in software mutation testing using firefly optimization algorithm

An efficient Software Source Code Metrics for Implementing for Software quality analysis

Using Tri-Relation Networks for Effective Software Fault-Proneness Prediction

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