A comparison of software effort estimation techniques: Using function points with neural networks, case-based reasoning and regression models

1997 ◽  
Vol 39 (3) ◽  
pp. 281-289 ◽  
Author(s):  
G.R. Finnie ◽  
G.E. Wittig ◽  
J-M. Desharnais
Keyword(s):  
Neural Networks ◽  
Regression Models ◽  
Case Based Reasoning ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Estimation Techniques ◽  
Function Points ◽  
Case Based

Examining the Feasibility of a Case-Based Reasoning Model for Software Effort Estimation

MIS Quarterly ◽  
1992 ◽  
Vol 16 (2) ◽  
pp. 155 ◽  
Author(s):  
Tridas Mukhopadhyay ◽  
Steven S. Vicinanza ◽  
Michael J. Prietula
Keyword(s):  
Case Based Reasoning ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Case Based

Web Development Effort Estimation

2011 ◽  
pp. 26-56 ◽  
Author(s):  
Emilia Mendes ◽  
Silvia Abrahão
Keyword(s):  
Multivariate Regression ◽  
Project Managers ◽  
Case Based Reasoning ◽  
Development Effort ◽  
Web Development ◽  
Effort Estimation ◽  
Estimation Techniques ◽  
Classification And Regression ◽  
Case Based

Effort models and effort estimates help project managers allocate resources, control costs and schedule, and improve current practices, leading to projects that are finished on time and within budget. In the context of Web development and maintenance, these issues are also crucial, and very challenging, given that Web projects have short schedules and a highly fluidic scope. Therefore, the objective of this chapter is to introduce the concepts related to Web effort estimation and effort estimation techniques. In addition, this chapter also details and compares, by means of a case study, three effort estimation techniques, chosen for this chapter because they have been to date the ones mostly used for Web effort estimation: Multivariate regression, Case-based reasoning, and Classification and Regression Trees. The case study uses data on industrial Web projects from Spanish Web companies.

scholarly journals A Data Pre-Processing Method for Software Effort Estimation Using Case-Based Reaso

2013 ◽  
Vol 16 (3) ◽  
Author(s):  
Katia Cristina A. Damaceno Borges ◽  
Iris Fabiana De Barcelos Tronto ◽  
Renato De Aquino Lopes ◽  
José Demisio Simões da Silva
Keyword(s):  
Software Development ◽  
Processing Method ◽  
Development Work ◽  
Case Based Reasoning ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Systematic Data ◽  
Essential Input ◽  
Comparable Accuracy ◽  
Case Based

Software effort estimates is an important part of software development work and provides essential input to project feasibility analyses, bidding, budgeting and planning. Analogy-based estimates models emerge as a promising approach, with comparable accuracy to arithmetic methods, and it is potentially easier to understand and apply. Studies show all the models are sensitive to the quality and availability data, thus requiring a systematic data treatment. In this paper, it is proposed a data pre-processing method for use in software effort estimate. The results of it on applying on applying Case Based Reasoning - CBR that enables us to enhance the precision of the estimates.

scholarly journals Using extended siamese networks to provide decision support in aquaculture operations

2021 ◽  
Author(s):  
Bjørn Magnus Mathisen ◽  
Kerstin Bach ◽  
Agnar Aamodt
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Similar Case ◽  
Decision Makers ◽  
Similarity Function ◽  
Case Based Reasoning ◽  
Similarity Learning ◽  
Reasoning System ◽  
Siamese Networks ◽  
Case Based

AbstractAquaculture as an industry is quickly expanding. As a result, new aquaculture sites are being established at more exposed locations previously deemed unfit because they are more difficult and resource demanding to safely operate than are traditional sites. To help the industry deal with these challenges, we have developed a decision support system to support decision makers in establishing better plans and make decisions that facilitate operating these sites in an optimal manner. We propose a case-based reasoning system called aquaculture case-based reasoning (AQCBR), which is able to predict the success of an aquaculture operation at a specific site, based on previously applied and recorded cases. In particular, AQCBR is trained to learn a similarity function between recorded operational situations/cases and use the most similar case to provide explanation-by-example information for its predictions. The novelty of AQCBR is that it uses extended Siamese neural networks to learn the similarity between cases. Our extensive experimental evaluation shows that extended Siamese neural networks outperform state-of-the-art methods for similarity learning in this task, demonstrating the effectiveness and the feasibility of our approach.

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