Experience has shown that poor strategy or bad tactics adopted when planning a software project influence the final quality of that product, even when the whole development process is undertaken with a quality approac...Experience has shown that poor strategy or bad tactics adopted when planning a software project influence the final quality of that product, even when the whole development process is undertaken with a quality approach. This paper addresses the quality attributes of the strategy and tactics of the software project plan that should be in place in order to deliver a good software product. It presents an initial work in which a set of required quality attributes is identified to evaluate the quality of the strategy and tactics of the software project plan, based on the Business Motivation Model (BMM) and the quality attributes available in the ISO 9126 standard on software product quality.展开更多
Maintaining software once implemented on the end-user side is laborious and,over its lifetime,is most often considerably more expensive than the initial software development.The prediction of software maintainability ...Maintaining software once implemented on the end-user side is laborious and,over its lifetime,is most often considerably more expensive than the initial software development.The prediction of software maintainability lias emerged as an important research topic to address industry expectations for reducing costs,in particular,maintenance costs.Researchers and practitioners have been working on proposing and identifying a variety of techniques ranging from statistical to machine learning(ML)for better prediction of software maintainability.This review has been carried out to analyze the empirical evidence on the accuracy of software product maintainability prediction(SPMP)using ML techniques.This paper analyzes and discusses the findings of 77 selected studies published from 2000 to 2018 according to the following criteria:maintainability prediction techniques,validation methods,accuracy criteria,overall accuracy of ML techniques,and the techniques offering the best performance.The review process followed the well-known syslematic review process.The results show that ML techniques are frequently used in predicting maintainability.In particular,artificial neural network(ANN),support vector machine/regression(SVM/R).regression&decision trees(DT),and fuzzy neuro fuzzy(FNF)techniques are more accurate in terms of PRED and MMRE.The N-fold and leave-one-out cross-validation methods,and the MMRE and PRED accuracy criteria are frequently used in empirical studies.In general,ML techniques outperformed non-machine learning techniques,e.g.,regression analysis(RA)techniques,while FNF outperformed SVM/R.DT.and ANN in most experiments.However,while many techniques were reported superior,no specific one can be identified as the best.展开更多
文摘Experience has shown that poor strategy or bad tactics adopted when planning a software project influence the final quality of that product, even when the whole development process is undertaken with a quality approach. This paper addresses the quality attributes of the strategy and tactics of the software project plan that should be in place in order to deliver a good software product. It presents an initial work in which a set of required quality attributes is identified to evaluate the quality of the strategy and tactics of the software project plan, based on the Business Motivation Model (BMM) and the quality attributes available in the ISO 9126 standard on software product quality.
文摘Maintaining software once implemented on the end-user side is laborious and,over its lifetime,is most often considerably more expensive than the initial software development.The prediction of software maintainability lias emerged as an important research topic to address industry expectations for reducing costs,in particular,maintenance costs.Researchers and practitioners have been working on proposing and identifying a variety of techniques ranging from statistical to machine learning(ML)for better prediction of software maintainability.This review has been carried out to analyze the empirical evidence on the accuracy of software product maintainability prediction(SPMP)using ML techniques.This paper analyzes and discusses the findings of 77 selected studies published from 2000 to 2018 according to the following criteria:maintainability prediction techniques,validation methods,accuracy criteria,overall accuracy of ML techniques,and the techniques offering the best performance.The review process followed the well-known syslematic review process.The results show that ML techniques are frequently used in predicting maintainability.In particular,artificial neural network(ANN),support vector machine/regression(SVM/R).regression&decision trees(DT),and fuzzy neuro fuzzy(FNF)techniques are more accurate in terms of PRED and MMRE.The N-fold and leave-one-out cross-validation methods,and the MMRE and PRED accuracy criteria are frequently used in empirical studies.In general,ML techniques outperformed non-machine learning techniques,e.g.,regression analysis(RA)techniques,while FNF outperformed SVM/R.DT.and ANN in most experiments.However,while many techniques were reported superior,no specific one can be identified as the best.
