TITLE:
A Hybrid Associative Classification Model for Software Development Effort Estimation
AUTHORS:
S. Saraswathi, N. Kannan
KEYWORDS:
Software Effort, Cost Estimation, Fuzzy Logic, Genetic Algorithm, Randomization Techniques
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.6,
May
17,
2016
ABSTRACT: A mathematical model that
makes use of data mining and soft computing techniques is proposed to estimate
the software development effort. The proposed model works as follows: The
parameters that have impact on the development effort are divided into groups
based on the distribution of their values in the available dataset. The
linguistic terms are identified for the divided groups using fuzzy functions,
and the parameters are fuzzified. The fuzzified parameters then adopt
associative classification for generating association rules. The association
rules depict the parameters influencing the software development effort. As the
number of parameters that influence the effort is more, a large number of rules
get generated and can reduce the complexity, the generated rules are filtered
with respect to the metrics, support and confidence, which measures the
strength of the rule. Genetic algorithm is then employed for selecting set of
rules with high quality to improve the accuracy of the model. The datasets such
as Nasa93, Cocomo81, Desharnais, Maxwell, and Finnish-v2 are used for
evaluating the proposed model, and various evaluation metrics such as Mean
Magnitude of Relative Error, Mean Absolute Residuals, Shepperd and MacDonell’s
Standardized Accuracy, Enhanced Standardized Accuracy and Effect Size are
adopted to substantiate the effectiveness of the proposed methods. The results
infer that the accuracy of the model is influenced by the metrics support,
confidence, and the number of association rules considered for effort
prediction.