Sumeet Kaur Sehra

Sumeet Kaur Sehra, Jasneet Kaur, Sukhjit Singh Sehra

Software effort estimation requires high accuracy, but accurate estimations are difficult to achieve. Increasingly, data mining is used to improve an organization's software process quality, e. g. the accuracy of effort estimations . There are a large number of different method combination exists for software effort estimation, selecting the most suitable combination becomes the subject of research in this paper. In this study, three simple preprocessors are taken (none, norm, log) and effort is measured using COCOMO model. Then results obtained from different preprocessors are compared and norm preprocessor proves to be more accurate as compared to other preprocessors.

Sumeet Kaur Sehra, Yadwinder Singh Brar, Navdeep Kaur

The effort invested in a software project is probably one of the most important and most analyzed variables in recent years in the process of project management. The limitation of algorithmic effort prediction models is their inability to cope with uncertainties and imprecision surrounding software projects at the early development stage. More recently attention has turned to a variety of machine learning methods, and soft computing in particular to predict software development effort. Soft computing is a consortium of methodologies centering in fuzzy logic, artificial neural networks, and evolutionary computation. It is important, to mention here, that these methodologies are complementary and synergistic, rather than competitive. They provide in one form or another flexible information processing capability for handling real life ambiguous situations. These methodologies are currently used for reliable and accurate estimate of software development effort, which has always been a challenge for both the software industry and academia. The aim of this study is to analyze soft computing techniques in the existing models and to provide in depth review of software and project estimation techniques existing in industry and literature based on the different test datasets along with their strength and weaknesses

Sumeet Kaur Sehra, Dr. Yadwinder Singh Brar, Dr. Navdeep Kaur

Effort Estimation has always been a challenging task for the Project managers. Many researchers have tried to help them by creating different types of models. This has been already proved that none is successful for all types of projects and every type of environment. Analytic Hierarchy Process has been identified as the tool that would help in Multi Criteria Decision Making. Researchers have identified that Analytic Hierarchy Process can be used for the comparison of effort estimation of different models and techniques. But the problem with traditional Analytic Hierarchy Process is its inability to deal with the imprecision and subjectivity in the pairwise comparison process. The motive of this paper is to propose Fuzzy Analytic Hierarchy Process, which can be used to rectify the subjectivity and imprecision of Analytic Hierarchy Process and can be used for selecting the type of Model best suited for estimating the effort for a given problem type or environment. Instead of single crisp value, Fuzzy Analytic Hierarchy Process uses a range of values to incorporate decision maker uncertainty. From this range, decision maker can select the value that reflects his confidence and also he can specify his attitude like optimistic, pessimistic or moderate. In this work, the comparison of Analytic Hierarchy Process and Fuzzy Analytic Hierarchy Process is concluded using a case study of selection of effort estimation model.