A differential evolution‐based model to estimate the software services development effort. Issue 1 (19th December 2015)

• Record Type: Journal Article
• Authors: Khatibi Bardsiri, Amid; Hashemi, Seyyed Mohsen
• Is Part Of: Journal of software. Volume 28:Issue 1(2016)
• Page Start: 57
• Page End: 77
• Publication Date: 2015-12-19
• Subjects: development effort -- analogy based estimation -- software services -- differential evolution algorithm; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.1765
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: Accurate estimation of software service development effort is a great challenge both in industry and for academia. The concept of effort is an important and effective parameter in process development and software service management. The reliable estimation of effort helps the project managers to allocate the resources better and manage cost and time so that the project will be finished in the determined time and budget. One of the most popular effort estimation methods is analogy‐based estimation (ABE) to compare a service with similar historical cases. Unfortunately, ABE is not capable of generating accurate results unless determining weights for service features. Therefore, this paper aims to make an efficient and reliable model through combining ABE method and differential evolution algorithm to estimate the software services development effort. In fact, the differential evolution algorithm was utilized for weighing features in the similarity function of the ABE method. This weighing process could help determining the importance level of the various service features and extracting the best similar historical case. The proposed hybrid model has been evaluated on two real datasets and two artificial datasets. The obtained results were compared with common effort estimation methods. This comparison showed more accuracy, faster convergence, and lower cost of the proposed model. Copyright © 2015 John Wiley & Sons, Ltd. Abstract : Examine 11 most popular effort‐estimation models. Propose new and efficient weighting model. Use many data sets and performance metrics. Help to choice and a better understanding of software‐service effort‐estimation methods.