The impact of customer expectation on software development effort estimates

2004 ◽  
Vol 22 (4) ◽  
pp. 317-325 ◽  
Author(s):  
Magne Jørgensen ◽  
Dag I.K. Sjøberg
Keyword(s):  
Software Development ◽  
Development Effort ◽  
Customer Expectation ◽  
Software Development Effort ◽  
The Impact

scholarly journals Improving Software Development effort estimating using Support Vector Regression and Feature Selection

2019 ◽  
Vol 8 (4) ◽  
pp. 399
Author(s):  
Abdelali ZAKRANI ◽  
Mustapha HAIN ◽  
Ali IDRI
Keyword(s):  
Feature Selection ◽  
Software Development ◽  
Support Vector Regression ◽  
Accurate Prediction ◽  
Support Vector ◽  
Development Effort ◽  
Effort Estimation ◽  
Planning And Scheduling ◽  
Software Development Effort ◽  
The Impact

Accurate and reliable software development effort estimation (SDEE) is one of the main concerns for project managers. Planning and scheduling a software projects using and inaccurate estimate may cause severe risks to software project under development such as delayed delivery, poor quality software, missing features. Therefore, accurate prediction of software effort plays an important role in the minimization of these risks that can lead to projects failure. Nowadays, application of artificial intelligence techniques has grown dramatically for predicting software effort. The researchers found that these techniques are suitable tools for accurate prediction. In this study, an improved model is designed for estimating software effort using support vector regression (SVR) and two feature selection (FS) methods. Prior to building model step, a preprocessing stage is performed by random forest or Boruta feature selection methods to remove unimportant features. Next, the SVR model is tuned by a grid search approach. The performance of model is then evaluated over eight well-known datasets through 30%holdout validation method. To show the impact of feature selection on the accuracy of SVR model, the proposed model was compared with SVR model without feature selection. The results indicated that SVR with feature selection outperforms SVR without FS in terms of the three accuracy measures used in this empirical study.

APPLYING EXPERT JUDGMENT TO IMPROVE AN INDIVIDUAL'S ABILITY TO PREDICT SOFTWARE DEVELOPMENT EFFORT

2012 ◽  
Vol 22 (04) ◽  
pp. 467-483 ◽  
Author(s):  
CUAUHTÉMOC LÓPEZ-MARTÍN ◽  
ALAIN ABRAN
Keyword(s):  
Software Development ◽  
Software Process ◽  
Development Effort ◽  
Academic Environment ◽  
Academic Institutions ◽  
Graduate Courses ◽  
Software Projects ◽  
Personal Software Process ◽  
Software Development Effort ◽  
Effort Prediction

Expert-based effort prediction in software projects can be taught, beginning with the practices learned in an academic environment in courses designed to encourage them. However, the length of such courses is a major concern for both industry and academia. Industry has to work without its employees while they are taking such a course, and academic institutions find it hard to fit the course into an already tight schedule. In this research, the set of Personal Software Process (PSP) practices is reordered and the practices are distributed among fewer assignments, in an attempt to address these concerns. This study involved 148 practitioners taking graduate courses who developed 1,036 software course assignments. The hypothesis on which it is based is the following: When the activities in the original PSP set are reordered into fewer assignments, the result is expert-based effort prediction that is statistically significantly better.

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