Identification of defect-prone classes in telecommunication software systems using design metrics

Janes, Andrea; Scotto, Marco; Pedrycz, Witold; Russo, Barbara; Stefanovic, Milorad; Succi, Giancarlo

doi:10.1016/j.ins.2005.12.002

Cited by 82 publications

(48 citation statements)

References 37 publications

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“…Interestingly, none of the studies found RFC insignificant but 5 of the studies did not measure RFC. [10], [19], [21], [17], [1], [22], [6], [25], [23], [3], [4], [2], [24], [27], [11], [29], [12], [9], [30], [31], [32], [36], [37] [8] [5], [28], [35], [34], [33] RFC [10], [19], [21], [17], [1], [22], [6], [11], [23], [3], [4], [8], [5], [24], [27], [11], [29], [12], [9], [30], [31], [32], [36], ...…”

Section: Rq1: Which Metric (S) Within the Ck Metric Suite And Sloc Ismentioning

confidence: 99%

“…Faults in software are errors introduced during the software development activity that can lead software to fail or not meeting customers' expectations. Though, it is difficult to find and fix faults before product release, the earlier this is done the, lower the costs and the higher the product quality would be [1][5] [7][8] [9][10] [11] [12] [13] [14] [15]. In software engineering, one way to assure software quality cost-effectively is the use of software metrics.…”

Section: Introductionmentioning

confidence: 99%

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2015

IJCSES

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Section: Rq1: Which Metric (S) Within the Ck Metric Suite And Sloc Ismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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2015

IJCSES

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“…Another study of using negative binomial regression analysis to predict fault-prone modules is reported by Janes et al [13] . In their study, they investigated the relation between object-oriented metrics and class defects in a real-time telecommunication system.…”

Section: Related Workmentioning

confidence: 99%

“…Among the many methods in predicting fault-prone software modules, negative binomial regression has been recently proposed and studied [12][13][14][15] . However, to the best of our knowledge, none of them provided detailed analysis about the performance of the prediction models, such as recall rate.…”

Section: Introductionmentioning

confidence: 99%

Using Negative Binomial Regression Analysis to Predict Software Faults: A Study of Apache Ant

2012

IJITCS

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Abstract-Negative binomial regression has been proposed as an approach to predicting fault-prone software modules. However, little work has been reported to study the strength, weakness, and applicability of this method. In this paper, we present a deep study to investigate the effectiveness of using negative binomial regression to predict fault-prone software modules under two different conditions, selfassessment and forward assessment. The performance of negative binomial regression model is also compared with another popular fault prediction model-binary logistic regression method. The study is performed on six versions of an open-source objected-oriented project, Apache Ant. The study shows (1) the performance of forward assessment is better than or at least as same as the performance of self-assessment; (2) in predicting fault-prone modules, negative binomial regression model could not outperform binary logistic regression model; and (3) negative binomial regression is effective in predicting multiple errors in one module.

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“…Numerous studies [6,7,8,9,10,11,12] show that coupling is a feature that has a significant impact on some critical software quality attributes such as reliability, portability, reusability, operability, flexibility, testability and maintainability, and, as a side effect, may introduce faults in software systems. In [13,14], is shown that high coupling tends to increase damage propagation when a vulnerability is exposed.…”

Section: Introductionmentioning

confidence: 99%

An Analytical Security Model for Existing Software Systems

Isazadeh¹,

Elgedawy²,

Karimpour³

et al. 2014

Appl. Math. Inf. Sci.

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Nowadays, evaluation of software security, as one of the important quality attributes, is of paramount importance. There are many software systems have not considered security in their design; this makes them vulnerable to security risks. Architecture is the most important consideration in software design that affects final quality of software. Quality attributes such as efficiency and reliability have been studied at software architecture level; however, no report has ever been provided about the effect of software architecture on security. The purpose of this paper is to propose a mathematical-based method for evaluating and quantifying software security using the coupling aspects of the software architecture. To achieve this goal, first, we show the relationship between coupling types and vulnerability using an empirical-based software engineering technique that adopts Mozilla Firefox Browser vulnerability data. Then, we propose a mathematical weighted relationship between coupling types and vulnerability, where regression statistical analysis and Mozilla Firefox vulnerability data are used to predicate the relationship coefficients. Finally, we extract software architecture using DAGC tool and then convert the extracted architecture into Discrete Time Markov chains, which are used to predict and compute the system over all vulnerability.

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Identification of defect-prone classes in telecommunication software systems using design metrics

Cited by 82 publications

References 37 publications

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Using Negative Binomial Regression Analysis to Predict Software Faults: A Study of Apache Ant

An Analytical Security Model for Existing Software Systems

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