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Why Do Software Projects Fail? Reasons and a Solution Using a Bayesian Classifier to Predict Potential Risk
Changsha, Hunan, China December 12-December 14
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PRDC.2005.6111th Pacific Rim International Sympos ...
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Tohru Kikuno, "Why Do Software Projects Fail? Reasons and a Solution Using a Bayesian Classifier to Predict Potential Risk," Pacific Rim International Symposium on Dependable Computing, IEEE, pp. 4, 11th Pacific Rim International Symposium on Dependable Computing (PRDC'05), 2005.
 
 
BibTex
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@article{ 10.1109/PRDC.2005.61,
author = {Tohru Kikuno},
title = {Why Do Software Projects Fail? Reasons and a Solution Using a Bayesian Classifier to Predict Potential Risk},
journal ={Pacific Rim International Symposium on Dependable Computing, IEEE},
volume = {0},
year = {2005},
isbn = {0-7695-2492-3},
pages = {4},
doi = {http://doi.ieeecomputersociety.org/10.1109/PRDC.2005.61},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Pacific Rim International Symposium on Dependable Computing, IEEE
TI - Why Do Software Projects Fail? Reasons and a Solution Using a Bayesian Classifier to Predict Potential Risk
SN - 0-7695-2492-3
SP
EP
A1 - Tohru Kikuno,
PY - 2005
KW - null
VL - 0
JA - Pacific Rim International Symposium on Dependable Computing, IEEE
ER -
 
Tohru Kikuno, Osaka University, Japan

The dependability of an information system critically relies on that of its software.

Reportedly, however, 70% of software projects fail in various ways. Obviously the system developed by a failed project can be problematic in assuring dependability. Clarifying the reasons why software projects fail and taking preventive measures are therefore a key to successful development of dependable information systems.

We present the results of our attempt to address this important issue. In this research attempt, (1) we designed a questionnaire that can be used to collect necessary data from software engineers. (2) We developed an analysis method that can identify reasons for project failures from the results of the questionnaire. This method uses a Bayesian classifier as its basis. (3) We conducted a large-scale experiment using field data obtained from industry. The results suggest that our method is useful to predict the success or failure of a software development project.

Citation:
Tohru Kikuno, "Why Do Software Projects Fail? Reasons and a Solution Using a Bayesian Classifier to Predict Potential Risk," prdc, pp.4, 11th Pacific Rim International Symposium on Dependable Computing (PRDC'05), 2005
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