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Scopus Q: Q2Subject: software metrics

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Now showing 1 - 4 of 4
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    Article
    On the Moment-Determinacy of Power Lindley Distribution and Some Applications To Software Metrics
    (Acad Brasileira de Ciencias, 2021) Khalleefah, Mohammed; Ostrovska, Sofiya; Turan, Mehmet
    The Lindley distribution and its numerous generalizations are widely used in statistical and engineering practice. Recently, a power transformation of Lindley distribution, called the power Lindley distribution, has been introduced by M. E. Ghitany et at who initiated the investigation of its properties and possible applications. In this article, new results on the power Lindley distribution are presented. The focus of this work is on the moment-(in)determinacy of the distribution for various values of the parameters. Afterwards, certain applications are provided to describe data sets of software metrics.
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    Citation - WoS: 4
    Document Type Definition (dtd) Metrics
    (Editura Acad Romane, 2011) Basci, Dilek; Misra, Sanjay
    In this paper, we present two complexity metrics for the assessment of schema quality written in Document Type Definition (DTD) language. Both "Entropy (E) metric: E(DTD)" and "Distinct Structured Element Repetition Scale (DSERS) metric: DSERS(DTD)" are intended to measure the structural complexity of schemas in DTD language. These metrics exploit a directed graph representation of schema document and consider the complexity of schema due to its similar structured elements and the occurrences of these elements. The empirical and theoretical validations of these metrics prove the robustness of the metrics.
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    Citation - WoS: 11
    Citation - Scopus: 12
    Estimating Quality of Javascript
    (Zarka Private Univ, 2012) Misra, Sanjay; Cafer, Ferid; Computer Engineering
    This paper proposes a complexity metric for Java script since JavaScript is the most popular scripting language that can run in all of the major web browsers. The proposed metric "JavaScript Cognitive Complexity Measure (JCCM)" is intended to assess the design quality of scripts. The metrics has been evaluated theoretically and validated empirically through real test cases. The metric has also been compared with other similar metrics. The theoretical, empirical validation and comparative study prove the worth and robustness of the metric.
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    Citation - WoS: 12
    Citation - Scopus: 13
    Techniques for Calculating Software Product Metrics Threshold Values: a Systematic Mapping Study
    (Mdpi, 2021) Mishra, Alok; Shatnawi, Raed; Catal, Cagatay; Akbulut, Akhan
    Several aspects of software product quality can be assessed and measured using product metrics. Without software metric threshold values, it is difficult to evaluate different aspects of quality. To this end, the interest in research studies that focus on identifying and deriving threshold values is growing, given the advantage of applying software metric threshold values to evaluate various software projects during their software development life cycle phases. The aim of this paper is to systematically investigate research on software metric threshold calculation techniques. In this study, electronic databases were systematically searched for relevant papers; 45 publications were selected based on inclusion/exclusion criteria, and research questions were answered. The results demonstrate the following important characteristics of studies: (a) both empirical and theoretical studies were conducted, a majority of which depends on empirical analysis; (b) the majority of papers apply statistical techniques to derive object-oriented metrics threshold values; (c) Chidamber and Kemerer (CK) metrics were studied in most of the papers, and are widely used to assess the quality of software systems; and (d) there is a considerable number of studies that have not validated metric threshold values in terms of quality attributes. From both the academic and practitioner points of view, the results of this review present a catalog and body of knowledge on metric threshold calculation techniques. The results set new research directions, such as conducting mixed studies on statistical and quality-related studies, studying an extensive number of metrics and studying interactions among metrics, studying more quality attributes, and considering multivariate threshold derivation.