Theoretical Validation of New Class Cohesion Metric Against Briand Properties

Mal, Sandip; Rajnish, Kumar

doi:10.1007/978-81-322-1665-0_58

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…CC metric satisfy necessary properties for class cohesion and comparison with other existing metrics given in Table II also described by [22]. The results show that CC metric satisfies all the properties given by Briand et al [11].…”

Section: Theoretical Validation Of Proposed Cohesion Metricssupporting

confidence: 61%

“…The metric CC proposed for measuring cohesion of a class that satisfies the following two requirements, viz., first, it gives values that can be uniquely interpreted in terms of cohesion, and, second, the values would be within a range of 0 to 1. The value 0 would signify minimum cohesion and 1 the maximum cohesion [22]. To evaluate CC, first calculate Cohesion Value of a global variable i th of a class (CV i ).…”

Section: Proposed Cohesion Metricsmentioning

confidence: 99%

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New Class Cohesion Metric: An Empirical View

Mal¹,

Rajnish²

2014

IJMUE

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Section: Theoretical Validation Of Proposed Cohesion Metricssupporting

confidence: 61%

Section: Proposed Cohesion Metricsmentioning

confidence: 99%

New Class Cohesion Metric: An Empirical View

Mal¹,

Rajnish²

2014

IJMUE

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“…Mal et al [4] proposed class cohesion (CC) metric and empirically validated against the open source software projects to found the effective quality factors. Their study concluded that CC continuously gave better correlation with Number Line of Code (NLOC) compared to other existing cohesion metrics.…”

Section: Review Of Literaturementioning

confidence: 99%

CCOM – A Communicational Cohesion Metric for Object-Oriented Programming

Vincentraj¹,

Ganesh²

2016

IJCA

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Object-oriented software metrics are the traditional quality assessment metrics that are aimed to ensure the goodness of the software. Almost all benefits of the OO programming have been addressed through software metrics except the factor that measures the types of cohesion incorporated in software modules. Software is measured as qualitative with the incurrence of high cohesion with low coupling. Hence, in our previous works, we have proposed certain cohesion metrics for assessing the functional and sequential level of cohesion in the software. As a continuation, in this work, a novel Communicational Cohesion Metric (CCOM) is proposed to evaluate the level of communicational cohesion of the software. The theoretical validation of the proposed CCOM is performed and compared with the traditional LCOM metric for elucidating the need for CCOM.

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“…We identify the error‐proneness by computing the cohesion values of the nodes. Through empirical studies, many researchers have validated that modules having low cohesion and high coupling values are more prone to errors. Thus, test cases executing such nodes have a high chance of detecting faults.…”

Section: Introductionmentioning

confidence: 99%

Regression test suite minimization using integer linear programming model

Panda¹,

Mohapatra

2017

Softw Pract Exp

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Software testers always face the dilemma of whether to retest the software with all the test cases or select a few of them on the basis of their fault detection ability. This paper introduces a novel approach to minimizing the test suite as an integer linear programming problem with optimal results. The minimization method uses the cohesion values of the program parts affected by the changes made to the program. The hypothesis is that the program parts with low cohesion values are more prone to errors. This assumption is validated on the mutation fault detection ability of the test cases. The experimental study carried out on 30 programs evaluates the effectiveness and usefulness of the proposed framework. The experimental results show that the minimized test suite can efficiently reveal the errors and ensure acceptable software quality. Copyright Figure 6. ILP encoding of the test data given in Table I.Figure 7. Percentage of faults detected by ST and M T of the example program given in Figure 2.restricted features for academic and research. The minimized test-suite (MT) obtained by this process for the test data in Table I is M T D ¹T 8; T 10º. Figure 7 shows the percentage of faults detected by ST and M T for the example program in Figure 2. The number of histograms in the figure denote the test cases in the corresponding test suite. It is evident that even after 60% minimization of ST , the percentage of faults detected by M T is 91.6% as compared with 100% fault detection by ST , which is comparable owing to the reduced number of test cases in M T . The graph implies that within constrained budget and time, the testers can still ensure good quality of the software with M T considering 100% fault detection is never possible in a real scenario. EXPERIMENTAL STUDYAfter demonstrating the effectiveness and usefulness of the proposed approach (refer Figure 7) for the example program given in Figure 2, we are posed with the following research questions (RQ): RQ1: Effectiveness. Does the minimized test suite actually guarantee acceptable quality (detection of faults) as compared with original test suite for all the experimental programs? RQ2: Usefulness. Is it feasible to generate the minimized test suite within acceptable time limits?

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Theoretical Validation of New Class Cohesion Metric Against Briand Properties

Cited by 6 publications

References 8 publications

New Class Cohesion Metric: An Empirical View

New Class Cohesion Metric: An Empirical View

CCOM – A Communicational Cohesion Metric for Object-Oriented Programming

Regression test suite minimization using integer linear programming model

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