{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T02:44:06Z","timestamp":1747190646458,"version":"3.40.5"},"reference-count":55,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,2,19]],"date-time":"2021-02-19T00:00:00Z","timestamp":1613692800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,2,19]]},"abstract":"<jats:p>Software metrics which are language-dependent are proposed as quantitative measures to assess internal quality factors for both method and class levels like cohesion and complexity. The external quality factors like reliability and maintainability are in general predicted using different metrics of internal attributes. Literature review shows a lack of software metrics which are proposed for reliability measurement and prediction. In this context, a suite of four semantic language-independent metrics was proposed by Mili et al. (2014) to assess program redundancy using Shannon entropy measure. The main objective of these metrics is to monitor program reliability. Despite their important purpose, they are manually computed and only theoretically validated. Therefore, this paper aims to assess the redundancy metrics and empirically validate them as significant reliability indicators. As software reliability is an external attribute that cannot be directly evaluated, we employ other measurable quality factors that represent direct reflections of this attribute. Among these factors, defect density is widely used to measure and predict software reliability based on software metrics. Therefore, a linear regression technique is used to show the usefulness of these metrics as significant indicators of software defect density. A quantitative model is then proposed to predict software defect density based on redundancy metrics in order to monitor software reliability.<\/jats:p>","DOI":"10.1155\/2021\/8325417","type":"journal-article","created":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T00:06:41Z","timestamp":1613779601000},"page":"1-20","source":"Crossref","is-referenced-by-count":1,"title":["An Empirical Assessment and Validation of Redundancy Metrics Using Defect Density as Reliability Indicator"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9938-2384","authenticated-orcid":true,"given":"Dalila","family":"Amara","sequence":"first","affiliation":[{"name":"Universit\u00e9 de Tunis, Institut Sup\u00e9rieur De Gestion De Tunis, SMART Lab, Tunis, Tunisia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9940-304X","authenticated-orcid":true,"given":"Ezzeddine","family":"Fatnassi","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Tunis, Institut Sup\u00e9rieur De Gestion De Tunis, SMART Lab, Tunis, Tunisia"},{"name":"Insitut des Hautes Etudes de Tunis, Tunis, Tunisia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5657-4682","authenticated-orcid":true,"given":"Latifa","family":"Ben Arfa Rabai","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Tunis, Institut Sup\u00e9rieur De Gestion De Tunis, SMART Lab, Tunis, Tunisia"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2015.01.057"},{"key":"2","first-page":"2910","article-title":"Iec25010: 2011 systems and software engineering\u2013systems and software quality requirements and evaluation (square)\u2013system and software quality models","volume":"34","author":"I. 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