Open AccessDetermining the Risky Software Projects using Artificial Neural Networks
Author(s) -
Etkin Sakucoglu,
Laman Valizada,
Ayse Buharali Olcaysoy,
Oya Kalıpsız
Publication year - 2022
Publication title -
international journal of software engineering and applications
Language(s) - English
Resource type - Journals
eISSN - 0976-2221
pISSN - 0975-9018
DOI - 10.5121/ijsea.2022.13201
Subject(s) - artificial neural network , backpropagation , computer science , artificial intelligence , software , set (abstract data type) , machine learning , data mining , naive bayes classifier , data set , support vector machine , programming language
Determining risky software projects early is a very important factor for project success. In this study it is aimed to choose the most correctly resulting modelling method that will be useful for early prediction of risky software projects to help companies to avoid losing time and money on unsuccessful projects and also facing legal requirements because of not being able to fullfill their responsibilites to their customers While making the research for this subject, it is seen that in previous researches, usually traditional modelling techniques were preferred. But it is observed that these methods were mostly resulted with high misclassification ratio. To overcome this problem, this study proposes a three-layered neural network (NN) architecture with a backpropagation algorithm. NN architecture was trained by using two different data sets which were OMRON data set (collected by OMRON) and 2016-2020 ES.LV data set (collected by the authors) separately. For the made of this study firstly the most relevant classification method (Gaussian Naive Bayes Algorithm) and the most relevant neural network method (Scaled Conjugate Gradient Backpropagation Algorithm) was chosen and both data sets were trained by using each method seperately for the purpose of observing which type of modelling architecture would give better results. Experimental results of this study showed that the neural network approach is useful for predicting whether a project is risky or not risky.