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ASSIGNMENT ID
29820
SUBJECT AREA Computer Science
DOCUMENT TYPE Research Paper
CREATED ON 22nd June 2016
COMPLETED ON 30th June 2016
PRICE
$40
18 offers received.
Expert hired: Muhammad Adil Khan

Software Metrics Data Analysis in Implementing Agile Process

paper need to have the following format need to be 1.0 .abstract and 2.0 Introduction - figures if any pl included 3.0 Literature survey (APA format) - figures if any pl included 4.0 Scope and plan of study also survey plan - figures if any pl included 5.0 data collection - figures if any pl included 6.0 data analysis - figures if any pl included 7.0 results and discussions including study as well as survey - tables to be included-- figures if any pl included 8.0 relationships like graphs and 9.0 Conclusions 10..0 Further work suggested and 11. References at least 10 ( APA format) --------------------------------- ----------------------------------- Empirical validation of software metrics suites to predict fault proneness in object-oriented (OO) components is essential to ensure their practical use in industrial settings. In this paper, we empirically validate three OO metrics suites for their ability to predict software quality in terms of fault-proneness: the Chidamber and Kemerer (CK) metrics, Abreu's Metrics for Object-Oriented Design (MOOD), and Bansiya and Davis' Quality Metrics for Object-Oriented Design (QMOOD). Some CK class metrics have previously been shown to be good predictors of initial OO software quality. However, the other two suites have not been heavily validated except by their original proposers. Here, we explore the ability of these three metrics suites to predict fault-prone classes using defect data for six versions of Rhino, an open-source implementation of JavaScript written in Java. We conclude that the CK and QMOOD suites contain similar components and produce statistical models that are effective in detecting error-prone classes. We also conclude that the class components in the MOOD metrics suite are not good class fault-proneness predictors. Analyzing multivariate binary logistic regression models across six Rhino versions indicates these models may be useful in assessing quality in OO classes produced using modern highly iterative or agile software development processes.
paper need to have the following format need to be 1.0 .abstract and 2.0 Introduction - figures if any pl included 3.0 Literature survey (APA format) - figures if any pl included 4.0 Scope and plan of study also survey plan - figures if any pl included 5.0 data collection - figures if any pl included 6.0 data analysis - figures if any pl included 7.0 results and discussions including study as well as survey - tables to be included-- figures if any pl included 8.0 relationships like graphs and 9.0 Conclusions 10..0 Further work suggested and 11. References at least 10 ( APA format) --------------------------------- ----------------------------------- Empirical validation of software metrics suites to predict fault proneness in object-oriented (OO) components is essential to ensure their practical use in industrial settings. In this paper, we empirically validate three OO metrics suites for their ability to predict software quality in terms of fault-proneness: the Chidamber and Kemerer (CK) metrics, Abreu's Metrics for Object-Oriented Design (MOOD), and Bansiya and Davis' Quality Metrics for Object-Oriented Design (QMOOD). Some CK class metrics have previously been shown to be good predictors of initial OO software quality.
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22 June 2016
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22 June 2016
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22 June 2016
User contacted expert Muhammad Adil Khan
22 June 2016
User hired expert Muhammad Adil Khan who offered a price of $40 for the project and has experience doing similar projects
30 June 2016
The expert completed the project Software Metrics Data Analysis in Implementing Agile Process for 8 days, meeting the deadline
30 June 2016
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