Using Evolutionary Based Approaches to Estimate Software Development Effort

2010 ◽  
pp. 13-28 ◽  
Author(s):  
Filomena Ferrucci ◽  
Carmine Gravino ◽  
Rocco Oliveto ◽  
Federica Sarro
Keyword(s):  
Genetic Algorithms ◽  
Software Development ◽  
Empirical Studies ◽  
Future Research ◽  
Development Effort ◽  
Effort Estimation ◽  
Research Directions ◽  
Software Development Effort ◽  
Critical Activity ◽  
Future Research Directions

Software development effort estimation is a critical activity for the competitiveness of a software company; it is crucial for planning and monitoring project development and for delivering the product on time and within budget. In the last years, some attempts have been made to apply search-based approaches to estimate software development effort. In particular, some genetic algorithms have been defined and some empirical studies have been performed with the aim of assessing the effectiveness of the proposed approaches for estimating software development effort. The results reported in those studies seem to be promising. The objective of this chapter is to present a state of the art in the field by reporting on the most significant empirical studies undertaken so far. Furthermore, some suggestions for future research directions are also provided.

The Application of Meta-Heuristic Algorithms to Improve the Performance of Software Development Effort Estimation Models

2015 ◽  
Vol 6 (4) ◽  
pp. 39-68 ◽  
Author(s):  
Maryam Hassani Saadi ◽  
Vahid Khatibi Bardsiri ◽  
Fahimeh Ziaaddini
Keyword(s):  
Software Development ◽  
Optimization Algorithms ◽  
Heuristic Optimization ◽  
Development Effort ◽  
Special Role ◽  
Effort Estimation ◽  
Software Projects ◽  
Software Development Effort ◽  
Estimation Models ◽  
Software Development Effort Estimation

One of the major activities in effective and efficient production of software projects is the precise estimation of software development effort. Estimation of the effort in primary steps of software development is one of the most important challenges in managing software projects. Some reasons for these challenges such as: discordant software projects, the complexity of the manufacturing process, special role of human and high level of obscure and unusual features of software projects can be noted. Predicting the necessary efforts to develop software using meta-heuristic optimization algorithms has made significant progressions in this field. These algorithms have the potent to be used in estimation of the effort of the software. The necessity to increase estimation precision urged the authors to survey the efficiency of some meta-heuristic optimization algorithms and their effects on the software projects. To do so, in this paper, they investigated the effect of combining various optimization algorithms such as genetic algorithm, particle swarm optimization algorithm and ant colony algorithm on different models such as COCOMO, estimation based on analogy, machine learning methods and standard estimation models. These models have employed various data sets to evaluate the results such as COCOMO, Desharnais, NASA, Kemerer, CF, DPS, ISBSG and Koten & Gary. The results of this survey can be used by researchers as a primary reference.

Sign in / Sign up

Export Citation Format

Share Document