Software Complexity Prediction by Using Basic Attributes

Autor:	Rasha Gaffer M. Helali
Rok vydání:	2016
Předmět:	Structure (mathematical logic) Computer science business.industry media_common.quotation_subject Programming complexity FURPS computer.software_genre Task (project management) Software Software construction Feature (machine learning) Quality (business) Data mining business computer media_common
Zdroj:	International Journal of Advanced Engineering Research and Science. 3:120-124
ISSN:	2456-1908 2349-6495
DOI:	10.22161/ijaers/3.11.21
Popis:	Software complexity is one of the important quality attribute that affect the success of software. Predicting such attribute is a difficult task for software engineers. Current used measures for computing complexity are not sufficient. Data mining can be applied to software data to explore useful interesting patterns. In this paper we present a simple data mining based prediction model to predict software complexity based on some basic attributes. The article starts by considering the correlation between different features that describes software code structure then selecting some of these features to be used for complexity prediction. Results reveal the ability to use branching count feature as strong predictor of complexity.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::002799b0871245907b7b13d6e2bc7a11 https://doi.org/10.22161/ijaers/3.11.21 Zobrazit plný text záznamu