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First we predict reliabilities of the components of a system. For this, we categorize the different kinds of faults that can occur in a component during its development and identify the design metrics that correlate to these faults. We construct a Bayesian Belief Network (BBN) model to predict reliabilities of the components using the identified design metrics. Based on predicted reliabilities and usage frequencies of the components of a system, we determine the reliability of the system. The applicability of our proposed model is illustrated through a case study. Results obtained from our case study indicate the effectiveness of our approach<\/jats:p>","DOI":"10.1145\/1874391.1874403","type":"journal-article","created":{"date-parts":[[2010,12,1]],"date-time":"2010-12-01T20:18:10Z","timestamp":1291234690000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["An approach for early prediction of software reliability"],"prefix":"10.1145","volume":"35","author":[{"given":"Sirsendu","family":"Mohanta","sequence":"first","affiliation":[{"name":"IIT Kharagpur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gopika","family":"Vinod","sequence":"additional","affiliation":[{"name":"Bhabha Atomic Research Centre, Mumbai, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A. 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