Software Defects Prediction Using by Ravi Kumar (7 results)
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Taschenbuch. Condition: Neu. Software Defects Prediction Using Soft Computing Techniques | T. Ravi Kumar (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786205487907 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbiet…er: preigu.
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Published by LAP LAMBERT Academic Publishing Aug 2022 2022
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Software quality and reliability has been uncompromising attribute in the software development. The software error can be a small deviation in the process, an improper definition of the requirement, or misrepresentation of the definitio…n. To avoid the lateral cost of the software we need to evaluate the software defects in the different phases of the software development life cycle.Software defect density prediction in each phase of the software development life cycle (SDLC) is critical to build a dependable software product. In the model, the defect density indicator in the requirement analysis, design, coding and testing phase is predicted using software metrics of these corresponding phases.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 164 pp. Englisch.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Software quality and reliability has been uncompromising attribute in the software development. The software error can be a small deviation in the process, an improper definition of the requirement, or misrepresentation of the definition…. To avoid the lateral cost of the software we need to evaluate the software defects in the different phases of the software development life cycle.Software defect density prediction in each phase of the software development life cycle (SDLC) is critical to build a dependable software product. In the model, the defect density indicator in the requirement analysis, design, coding and testing phase is predicted using software metrics of these corresponding phases.
