%0 Conference Paper %1 4456863 %A Baskeles, B. %A Turhan, B. %A Bener, A. %B Computer and information sciences, 2007. iscis 2007. 22nd international symposium on %D 2007 %K myown %P 1-6 %R 10.1109/ISCIS.2007.4456863 %T Software effort estimation using machine learning methods %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4456863 %X In software engineering, the main aim is to develop projects that produce the desired results within limited schedule and budget. The most important factor affecting the budget of a project is the effort. Therefore, estimating effort is crucial because hiring people more than needed leads to a loss of income and hiring people less than needed leads to an extension of schedule. The main objective of this research is making an analysis of software effort estimation to overcome problems related to it: budget and schedule extension. To accomplish this, we propose a model that uses machine learning methods. We evaluate these models on public datasets and data gathered from software organizations in Turkey. It is found out in the experiments that the best method for a dataset may change and this proves the point that the usage of one model cannot always produce the best results.

@inproceedings{4456863, abstract = {In software engineering, the main aim is to develop projects that produce the desired results within limited schedule and budget. The most important factor affecting the budget of a project is the effort. Therefore, estimating effort is crucial because hiring people more than needed leads to a loss of income and hiring people less than needed leads to an extension of schedule. The main objective of this research is making an analysis of software effort estimation to overcome problems related to it: budget and schedule extension. To accomplish this, we propose a model that uses machine learning methods. We evaluate these models on public datasets and data gathered from software organizations in Turkey. It is found out in the experiments that the best method for a dataset may change and this proves the point that the usage of one model cannot always produce the best results.}, added-at = {2015-09-17T22:27:28.000+0200}, author = {Baskeles, B. and Turhan, B. and Bener, A.}, biburl = {https://www.bibsonomy.org/bibtex/211c8c57ec737b2c4cfe0b05c3c61eceb/burak.turhan}, booktitle = {Computer and information sciences, 2007. iscis 2007. 22nd international symposium on}, description = {IEEE Xplore Abstract - Software effort estimation using machine learning methods}, doi = {10.1109/ISCIS.2007.4456863}, interhash = {0f1c9102e837ce1d9e20a3090d3827ae}, intrahash = {11c8c57ec737b2c4cfe0b05c3c61eceb}, keywords = {myown}, month = nov, pages = {1-6}, timestamp = {2015-09-17T22:27:28.000+0200}, title = {Software effort estimation using machine learning methods}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4456863}, year = 2007 }