Junya Debari,Osamu Mizuno,Tohru Kikuno,Nahomi Kikuchi,Masayuki Hirayama
For software project management, it is very important to identify risk factors which make project into runaway. In this study, we propose a method to extract improvement action items for a software project by applying association rule mining to the software project repository for a metric of “cost overrun”. We first mine a number of association rules affecting cost overrun. We then group compatible rules, which include several common metrics having different values, from the mined rules and extract improvement action items of project improvement. In order to evaluate the applicability of our method, we applied our method to the project data repository collected from plural companies in Japan. The result of experiment showed that project improvement actions for cost overrun were semi-automatically extracted from the mined association rules. We can confirm feasibility of our method by comparing these actions with the results in the previous research.
30%
Junya Debari,Osamu Mizuno,Tohru Kikuno,Nahomi Kikuchi,Masayuki Hirayama
Junya Debari,Osamu Mizuno,Tohru Kikuno,Nahomi Kikuchi,Masayuki Hirayama
Proc. of International Conference on Software Process 2008 (ICSP2008)
525
5
Leipzig, Germany
51-62
1
On Deriving Actions for Improving Cost Overrun by Applying Association Rule Mining to Industrial Project Repository
http://www.springerlink.com/content/q47382h1rr12751w/
LNCS 5006
2008
Sousuke Amasaki,Yasuhiro Hamano,Osamu Mizuno,Tohru Kikuno
In this paper, characteristics of a runaway project are revealed based on combination of risk factors
which appear in the project. Concretely, an association rule mining technique is applied with an actual
questionnaire data to induce rules that associate combination of risk factors with runaway status of
software projects. Furthermore, the induced rules are integrated and reduced based on a certain rule
obtained from experts’ perception to simplify the representation of characteristics of a runaway project.
Then, for confirming the effectiveness of this characterization, it is evaluated how many runaway
projects in distinct data set were identified by the reduced rules. The result of the experiment suggested
that the induced rules are effective to characterize runaway projects.
47.2%, 26/55
Sousuke Amasaki,Yasuhiro Hamano,Osamu Mizuno,Tohru Kikuno
Sousuke Amasaki,Yasuhiro Hamano,Osamu Mizuno,Tohru Kikuno
Proc. of 7th International Conference on Product Focused Software Process Improvement (PROFES2006)
467
6
Amsterdam, The Netherlands
402-407
1
Characterization of Runaway Software Projects Using Association Rule Mining
http://www.springerlink.com/content/l6357661h6p8tk62/
LNCS 4034
2006
Junya Debari,Kenichi Ogata,Tohru Kikuno,Osamu Mizuno,Nahomi Kikuchi,Masayuki Hirayama
出張 純也, 尾形 憲一,菊野 亨, 水野 修, 菊地 奈穂美, 平山 雅之
Junya Debari,Kenichi Ogata,Tohru Kikuno,Osamu Mizuno,Nahomi Kikuchi,Masayuki Hirayama
情報処理学会創立50周年記念全国大会(第72回全国大会)
599
3
東京大学
5B-1
3
相関ルールマイニングを利用したソフトウェアプロジェクト混乱要因の関連性に関する調査
Extracting Relationships between Risk Factors of Software Projects with Association Rule Mining
2010
Tetsuya Iida,Osamu Mizuno,Tohru Kikuno,Sachie Yoshioka,Yoshiyuki Anan,Mataharu Tanaka
本報告では,ソフトウェア出荷後に市場で障害を発生させるプロジェクトの条件を特定すべく,開発プロジェクトから収集されたデータに相関ルールマイニングを実施した.具体的には,開発現場から出された複数の障害に対する仮説に対して,プロジェクトから収集されたデータへのマイニングを実施し,目的の仮説を表す相関ルールの抽出を行った.このマイニングの結果,いくつかの仮説に対してはその裏付けとなる相関ルールの検出に成功した.一例として,規模の大きな新規プロジェクトでは,全工程を通じての検出不具合数,および,レビューでの検出不具合数がある一定値を上回ったプロジェクトでは,障害が発生しやすいことなどが確認された.
飯田 哲也,水野 修,菊野 亨,吉岡 幸恵,阿南 佳之,田中 又治
Tetsuya Iida,Osamu Mizuno,Tohru Kikuno,Sachie Yoshioka,Yoshiyuki Anan,Mataharu Tanaka
電子情報通信学会技術報告
Technical Report of IEICE
557
1
東京
384, KBSE2008-50
79-84
3
ソフトウェアメトリクスのデータマイニングによる障害発生要因特定
An Analysis of Causes of Faults After Release by Rule Mining on Software Metrics
108
2009
Junya Debari,Osamu Mizuno,Tohru Kikuno,Nahomi Kikuchi,Masayuki Hirayama
SS2007-36
出張 純也,水野 修,菊野 亨,菊地 奈穂美,平山 雅之
Junya Debari,Osamu Mizuno,Tohru Kikuno,Nahomi Kikuchi,Masayuki Hirayama
電子情報通信学会技術研究報告
Technical Report of IEICE
523
10
宮城大学
275, SS2007-36
35-40
3
相関ルールマイニングによる企業横断データにおける不具合工数密度の分析
Analysis of Fault Density by Association Rule Mining Using Cross-Company Data
107
2007