{"title":"基于软计算技术的敏捷软件开发中软件成本估算综述","authors":"Saurabh Bilgaiyan, Samaresh Mishra, M. Das","doi":"10.1109/CINE.2016.27","DOIUrl":null,"url":null,"abstract":"For a successful software project, accurate prediction of its overall effort and cost estimation is a very much essential task. Software projects have evolved through a number of development models over the last few decades. Hence, to cover an accurate measurement of the effort and cost for different software projects based on different development models having new and innovative phases of software development, is a crucial task to be done. An accurate prediction always leads to a successful software project within the budget with no delay, but any percentage of misconduct in the overall effort and cost estimate may lead to a project failure in terms of delivery time, budget or features. Software industries have adopted various development models based on the project requirements and organization's capabilities. Due to adaptability to changes in a software project, agile software development model has become a much successful and popular framework for development over the last decade. The customer is involved as an active participant in the development using an agile framework. Hence, changes can occur at any phase of development and they can be dynamic in nature. That is why an accurate prediction of effort and cost of such projects is a crucial task to be done as the complexity of overall development structure is increased with the time. Soft computing techniques have proven that they are one of the best problem solving techniques in such scenarios. Such techniques are more flexible and presence of bio-intelligence increases their accuracy. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), Fuzzy Inference Systems (FIS), etc. are applied successfully for estimation of cost and effort of agile based software projects. This paper deals with such soft computing techniques and provides a detailed and analytical overview of such methods. It also provides the future scope and possibilities to explore such techniques on the basis of survey provided by this paper.","PeriodicalId":142174,"journal":{"name":"2016 2nd International Conference on Computational Intelligence and Networks (CINE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":"{\"title\":\"A Review of Software Cost Estimation in Agile Software Development Using Soft Computing Techniques\",\"authors\":\"Saurabh Bilgaiyan, Samaresh Mishra, M. Das\",\"doi\":\"10.1109/CINE.2016.27\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For a successful software project, accurate prediction of its overall effort and cost estimation is a very much essential task. Software projects have evolved through a number of development models over the last few decades. Hence, to cover an accurate measurement of the effort and cost for different software projects based on different development models having new and innovative phases of software development, is a crucial task to be done. An accurate prediction always leads to a successful software project within the budget with no delay, but any percentage of misconduct in the overall effort and cost estimate may lead to a project failure in terms of delivery time, budget or features. Software industries have adopted various development models based on the project requirements and organization's capabilities. Due to adaptability to changes in a software project, agile software development model has become a much successful and popular framework for development over the last decade. The customer is involved as an active participant in the development using an agile framework. Hence, changes can occur at any phase of development and they can be dynamic in nature. That is why an accurate prediction of effort and cost of such projects is a crucial task to be done as the complexity of overall development structure is increased with the time. Soft computing techniques have proven that they are one of the best problem solving techniques in such scenarios. Such techniques are more flexible and presence of bio-intelligence increases their accuracy. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), Fuzzy Inference Systems (FIS), etc. are applied successfully for estimation of cost and effort of agile based software projects. This paper deals with such soft computing techniques and provides a detailed and analytical overview of such methods. It also provides the future scope and possibilities to explore such techniques on the basis of survey provided by this paper.\",\"PeriodicalId\":142174,\"journal\":{\"name\":\"2016 2nd International Conference on Computational Intelligence and Networks (CINE)\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"27\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 2nd International Conference on Computational Intelligence and Networks (CINE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CINE.2016.27\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 2nd International Conference on Computational Intelligence and Networks (CINE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINE.2016.27","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Review of Software Cost Estimation in Agile Software Development Using Soft Computing Techniques
For a successful software project, accurate prediction of its overall effort and cost estimation is a very much essential task. Software projects have evolved through a number of development models over the last few decades. Hence, to cover an accurate measurement of the effort and cost for different software projects based on different development models having new and innovative phases of software development, is a crucial task to be done. An accurate prediction always leads to a successful software project within the budget with no delay, but any percentage of misconduct in the overall effort and cost estimate may lead to a project failure in terms of delivery time, budget or features. Software industries have adopted various development models based on the project requirements and organization's capabilities. Due to adaptability to changes in a software project, agile software development model has become a much successful and popular framework for development over the last decade. The customer is involved as an active participant in the development using an agile framework. Hence, changes can occur at any phase of development and they can be dynamic in nature. That is why an accurate prediction of effort and cost of such projects is a crucial task to be done as the complexity of overall development structure is increased with the time. Soft computing techniques have proven that they are one of the best problem solving techniques in such scenarios. Such techniques are more flexible and presence of bio-intelligence increases their accuracy. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), Fuzzy Inference Systems (FIS), etc. are applied successfully for estimation of cost and effort of agile based software projects. This paper deals with such soft computing techniques and provides a detailed and analytical overview of such methods. It also provides the future scope and possibilities to explore such techniques on the basis of survey provided by this paper.