• Eileen Christina Mercy
• Dr.T.R.Sivapirya
• C. Malarvizhi

Accurately predicting software development effort has been a conundrum that has piqued the interest of researchers ever since computer systems became mainstream. Unlike an assembly line that churns out tangible products, where effort required can be predicted with relative ease, there are a plethora of factors that collectively exert influence on software development effort. There are situational and contextual factors which play a part in determining software development effort. Some of the factors that influence software development effort are industry domain, language used, and development method and resource capability. This study is aimed at improvising on previous research work of correlation of key factors, to add another dimension to accurately rank the key features that influence software development effort. Existing studies have applied correlation, case studies and statistical techniques. The proposed work is a new approach to mine the optimal associations and to rank the most influencing factors. Common associations have been mined from various software development domains to discover the underlying relationship. Feature search is implemented to bring out the dominating factors and has compared with the results of the statistical methods. Using the additional modifiers like clustering and association to arrive at software effort is proven to increase the accuracy of software estimation by more than ninety percent using the same dataset. The proposed method identified the top ranking associations that would enhance the software development process.

Software Development Effort, Clustering, Association, industry domain

"Optimal Feature Search to rank crucial factors that influence software development through Predictive Association mining ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.40-47, May-2019, Available :http://www.jetir.org/papers/JETIR1905008.pdf

"Optimal Feature Search to rank crucial factors that influence software development through Predictive Association mining ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp40-47, May-2019, Available at : http://www.jetir.org/papers/JETIR1905008.pdf