Technical debt(TD)happens when project teams carry out technical decisions in favor of a short-term goal(s)in their projects,whether deliberately or unknowingly.TD must be properly managed to guarantee that its negati...Technical debt(TD)happens when project teams carry out technical decisions in favor of a short-term goal(s)in their projects,whether deliberately or unknowingly.TD must be properly managed to guarantee that its negative implications do not outweigh its advantages.A lot of research has been conducted to show that TD has evolved into a common problem with considerable financial burden.Test technical debt is the technical debt aspect of testing(or test debt).Test debt is a relatively new concept that has piqued the curiosity of the software industry in recent years.In this article,we assume that the organization selects the testing artifacts at the start of every sprint.Implementing the latest features in consideration of expected business value and repaying technical debt are among candidate tasks in terms of the testing process(test cases increments).To gain the maximum benefit for the organization in terms of software testing optimization,there is a need to select the artifacts(i.e.,test cases)with maximum feature coverage within the available resources.The management of testing optimization for large projects is complicated and can also be treated as a multi-objective problem that entails a trade-off between the agile software’s short-term and long-term value.In this article,we implement a multi-objective indicatorbased evolutionary algorithm(IBEA)for fixing such optimization issues.The capability of the algorithm is evidenced by adding it to a real case study of a university registration process.展开更多
To improve the maintenance and quality of software product lines,efficient configurations techniques have been proposed.Nevertheless,due to the complexity of derived and configured products in a product line,the confi...To improve the maintenance and quality of software product lines,efficient configurations techniques have been proposed.Nevertheless,due to the complexity of derived and configured products in a product line,the configuration process of the software product line(SPL)becomes timeconsuming and costly.Each product line consists of a various number of feature models that need to be tested.The different approaches have been presented by Search-based software engineering(SBSE)to resolve the software engineering issues into computational solutions using some metaheuristic approach.Hence,multiobjective evolutionary algorithms help to optimize the configuration process of SPL.In this paper,different multi-objective Evolutionary Algorithms like Non-Dominated Sorting Genetic algorithms II(NSGA-II)and NSGA-III and Indicator based Evolutionary Algorithm(IBEA)are applied to different feature models to generate optimal results for large configurable.The proposed approach is also used to generate the optimized test suites with the help of different multi-objective Evolutionary Algorithms(MOEAs).展开更多
基金The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQUyouracademicnumberDSRxx).
文摘Technical debt(TD)happens when project teams carry out technical decisions in favor of a short-term goal(s)in their projects,whether deliberately or unknowingly.TD must be properly managed to guarantee that its negative implications do not outweigh its advantages.A lot of research has been conducted to show that TD has evolved into a common problem with considerable financial burden.Test technical debt is the technical debt aspect of testing(or test debt).Test debt is a relatively new concept that has piqued the curiosity of the software industry in recent years.In this article,we assume that the organization selects the testing artifacts at the start of every sprint.Implementing the latest features in consideration of expected business value and repaying technical debt are among candidate tasks in terms of the testing process(test cases increments).To gain the maximum benefit for the organization in terms of software testing optimization,there is a need to select the artifacts(i.e.,test cases)with maximum feature coverage within the available resources.The management of testing optimization for large projects is complicated and can also be treated as a multi-objective problem that entails a trade-off between the agile software’s short-term and long-term value.In this article,we implement a multi-objective indicatorbased evolutionary algorithm(IBEA)for fixing such optimization issues.The capability of the algorithm is evidenced by adding it to a real case study of a university registration process.
基金The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQUyouracademicnumberDSRxx).
文摘To improve the maintenance and quality of software product lines,efficient configurations techniques have been proposed.Nevertheless,due to the complexity of derived and configured products in a product line,the configuration process of the software product line(SPL)becomes timeconsuming and costly.Each product line consists of a various number of feature models that need to be tested.The different approaches have been presented by Search-based software engineering(SBSE)to resolve the software engineering issues into computational solutions using some metaheuristic approach.Hence,multiobjective evolutionary algorithms help to optimize the configuration process of SPL.In this paper,different multi-objective Evolutionary Algorithms like Non-Dominated Sorting Genetic algorithms II(NSGA-II)and NSGA-III and Indicator based Evolutionary Algorithm(IBEA)are applied to different feature models to generate optimal results for large configurable.The proposed approach is also used to generate the optimized test suites with the help of different multi-objective Evolutionary Algorithms(MOEAs).
