Yasuhiro Hamano,Sousuke Amasaki,Osamu Mizuno,Tohru Kikuno
本論文では,我々はソフトウェアプロジェクトを混乱プロジェクトに陥れるリスク要因の特定
を目指す.まず最初に相関ルールマイニングを実際のソフトウェア開発の現場から集めた質問
データに適用する.その結果,リスク要因に関するいくつかの特徴的なルールを抽出した.
次に,抽出できたルールの有用性を評価するためにロジスティック回帰分析で抽出されたリス
ク要因との比較を行った.比較の結果,抽出したルールとそのルールから導き出せるリスク要
因が混乱プロジェクトの特定に有用であることを確認した.
浜野 康裕,天嵜 聡介,水野 修,菊野 亨
Yasuhiro Hamano,Sousuke Amasaki,Osamu Mizuno,Tohru Kikuno
480
コンピュータソフトウェア
JSSST Computer Software
2
2
79-87
0
相関ルールマイニングによるソフトウェア開発プロジェクト中のリスク要因の分析
Application of Association Rules Mining to Analysis of Risk Factors in Software Development Projects
http://www.jstage.jst.go.jp/article/jssst/24/2/24_2_79/_article/-char/ja/
24
2007
Tohru Kikuno,Osamu Mizuno,Sousuke Amasaki
本研究ではプロジェクトの混乱を予測する手法を提案する.
菊野 亨,水野 修,天嵜 聡介
Tohru Kikuno,Osamu Mizuno,Sousuke Amasaki
460
日本信頼性学会誌
The journal of Reliability Engineering Association of Japan
10
招待論文
7
471-482
0
定量的プロジェクトマネジメント〜メトリクスデータ利用の新技術
Quantitative Project Management for Software Development: A Simple Bayesian Classifier Using Software Metric Data
27
2005
Sousuke Amasaki,Yasunari Takagi,Osamu Mizuno,Tohru Kikuno
Recently, software development projects have been required to produce highly reliable systems
within a short period and with low cost. In such situation, software quality prediction helps to
confirm that the software product satisfies required quality expectations. In this paper, by using
a Bayesian Belief Network (BBN), we try to construct a prediction model based on relationships
elicited from the embedded software development process. On the one hand, according to
a characteristic of embedded software development, we especially propose to classify test and
debug activities into two distinct activities on software and hardware. Then we call the proposed
model the BBN for an embedded software development process. On the other hand, we define
the BBN for a general software development process to be a model which does not consider
this classification of activity, but rather, merges them into a single activity.
Finally, we conducted experimental evaluations by applying these two BBNs to actual project data.
As the results of our experiments show, the BBN for the embedded software development process
is superior to the BBN for the general development process and is applicable effectively for
effective practical use.
Sousuke Amasaki,Yasunari Takagi,Osamu Mizuno,Tohru Kikuno
Sousuke Amasaki,Yasunari Takagi,Osamu Mizuno,Tohru Kikuno
441
0.242 (2005)
IEICE Trans. on Information and Systems
6
6
1134-1141
0
Constructing a Bayesian Belief Network to Predict Final Quality in Embedded System Development
http://search.ieice.org/bin/summary.php?id=e88-d_6_1134&category=D&year=2005&lang=E&abst=
E88-D
2005
Sousuke Amasaki,Takashi Yoshitomi,Osamu Mizuno,Yasunari Takagi,Tohru Kikuno
In typical software development, a software reliability growth model
(SRGM) is applied in each testing activity to determine the time to
finish the testing.
However, there are some cases in which the SRGM does not work
correctly. That is, the SRGM sometimes mistakes quality for poor
quality products. In order to tackle this problem, we apply time
series data collected from development to quality estimation.
First, we investigate the characteristics of the time series data on
the detected faults by observing the change of the number of
detected faults. Using the rank correlation coefficient, the data
are classified into four kinds of trends. Next, with the intention
of estimating software quality, we investigate the relationship
between the trends of the time series data and software
quality. Here, software quality is defined by the number of faults
detected during six months after shipment.
Finally, we find a relationship between the trends and metrics data
collected in the software design phase. Using logistic regression,
we statistically show that two review metrics in the design \&
coding phase can determine the trend.
Sousuke Amasaki,Takashi Yoshitomi,Osamu Mizuno,Yasunari Takagi,Tohru Kikuno
Sousuke Amasaki,Takashi Yoshitomi,Osamu Mizuno,Yasunari Takagi,Tohru Kikuno
434
0.529 (2005)
Software Quality Journal
6
2
177-193
0
A New Challenge for Applying Time Series Metrics Data to Software Quality Estimation
http://www.springerlink.com/content/l344q05u8681m7q2/
13
2005
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
Sousuke Amasaki,Yasunari Takagi,Osamu Mizuno,Tohru Kikuno
Sousuke Amasaki,Yasunari Takagi,Osamu Mizuno,Tohru Kikuno
Sousuke Amasaki,Yasunari Takagi,Osamu Mizuno,Tohru Kikuno
Poster Presentation in Doctoral Symposium, 27th International Conference on Software Engineering (ICSE2005)
445
5
St. Louis, MO, USA
1
Empirical Diagnosis of Software Projects by a Bayesian Classifier
2005
Sousuke Amasaki,Yasunari Takagi,Osamu Mizuno,Tohru Kikuno
To predict software quality, we must consider various factors
because software development consists of various activities, which
the software reliability growth model (SRGM) does not consider.
In this paper, we propose a model to predict the final quality of
a software product by using the Bayesian belief network (BBN) model.
By using the BBN, we can construct a prediction
model that focuses on the structure of the software development process
explicitly representing complex relationships between metrics, and
handling uncertain metrics, such as residual faults in the software
products.
In order to evaluate the constructed model, we perform an empirical
experiment based on the metrics data
collected from development projects in a certain company.
As a result of the empirical
evaluation, we confirm that the proposed model can predict the amount
of residual faults that the SRGM cannot handle.
20%, 41/200
Sousuke Amasaki,Yasunari Takagi,Osamu Mizuno,Tohru Kikuno
Sousuke Amasaki,Yasunari Takagi,Osamu Mizuno,Tohru Kikuno
Proc. of 14th International Symposium on Software Reliability Engineering (ISSRE2003)
408
11
Denver, CO, USA
215-226
1
A Bayesian Belief Network for Assessing the Likelihood of Fault Content
2003
Xiaoning Li,Sousuke Amasaki,Daisuke Shimoda,Osamu Mizuno,Tohru Kikuno
Xiaoning Li,Sousuke Amasaki,Daisuke Shimoda,Osamu Mizuno,Tohru Kikuno
Xiaoning Li,Sousuke Amasaki,Daisuke Shimoda,Osamu Mizuno,Tohru Kikuno
Proc. of 14th International Symposium on Software Reliability Engineering (ISSRE2003), Supplemental proceedings
413
11
Denver, CO, USA
335-336
1
Developing a Practical Software Project Simulator Based on System Dynamics Model
2003
Sousuke Amasaki,Takashi Yoshitomi,Osamu Mizuno,Tohru Kikuno,Yasunari Takagi
According to a progress of the software process improvement, the
time series data on the number of faults detected by the software
testing are collected extensively. In this paper, we
perform statistical analyses of relationships between the time
series data and the field quality of software products.
At first, we apply the rank correlation coefficient $\tau$ to the
time series data collected from actual software testing in a
certain company, and classify these data into four types of trends:
strict increasing, almost increasing, almost decreasing, and
strict decreasing. We then investigate, for each type of trend,
the field quality of software products developed by the
corresponding software projects. As a result of statistical
analyses, we showed that software projects having trend of almost or
strict decreasing in the number of faults detected by the software
testing could produce the software products with high quality.
Sousuke Amasaki,Takashi Yoshitomi,Osamu Mizuno,Tohru Kikuno,Yasunari Takagi
Sousuke Amasaki,Takashi Yoshitomi,Osamu Mizuno,Tohru Kikuno,Yasunari Takagi
Proc. of 11th Asian Test Symposium (ATS02)
139
11
Guam, USA.
272-277
1
Statistical analysis of time series data on the number of faults detected by software testing
2002
Yasuhiro Hamano, Sousuke Amasaki, Osamu Mizuno, Tohru Kikuno
浜野康裕,天嵜聡介,水野修,菊野亨
第12回ソフトウェア工学の基礎ワークショップ(FOSE2005)
449
11
仙台
87-96
2
ソフトウェア開発プロジェクト中のリスク要因に対するルールマイニングを利用した分析
2005
Osamu Mizuno,Sousuke Amasaki, Futoshi Yamanouchi, Tohru Kikuno, Yasunari Takagi
ソフトウェア開発の現場では,プロジェクトが混乱状態に陥らないようにあら
かじめ問題要因を探り,混乱の可能性を早期に予測することが望まれている.
これまでに,プロジェクトマネージャに対してアンケートを実施することによ
り問題要因を特定し,混乱という事象に対処するために問題要因のパラメータ
の値に回帰モデルやクラスタ分析を適用して混乱を推定する手法の提案をして
きた.しかし,プロジェクトの早期段階でパラメータの値が全て判明している
ことはまれであり,それが原因となってプロジェクトの早期段階での上記モデ
ルの適用は困難であった.
本研究ではソフトウェア開発プロジェクトが混乱するかどうかを,前もって収
集されているリスク要因の分析結果も利用して予測するモデルの提案を行う.
そのために,ベイジアンネットを用いた混乱予測モデルを作成する.ベ
イジアンネットを用いることにより,一部のパラメータの値が不明で
あっても事前に与えられた確率分布を利用して混乱の確率を算出できるよう
になる.次に,実際のソフトウェア開発現場から収集したデータを適用し,評
価実験を行う.実験の結果,プロジェクトの早期段階でも非常に高い精度で
混乱予測が可能となることを確認した.
水野修,天嵜聡介,山之内太,菊野亨,高木徳生
ソフトウェアシンポジウム2003論文集
384
7
弘前
ソフトウェア技術者協会
193-199
2
混乱プロジェクトの予測へのベイジアンネットの適用
2003
浜野 康裕,天嵜 聡介,水野 修,菊野 亨
Yasuhiro Hamano, Sousuke Amasaki, Osamu Mizuno, Tohru Kikuno
情報処理学会研究報告 ソフトウェア工学(SE)
498
11
大阪大学
125, 2006-SE-154
1-8
3
相関ルールマイニングを用いた混乱プロジェクトの特徴分析
2006
2006
Sousuke Amasaki, Osamu Mizuno, Tohru Kikuno
天嵜聡介,水野修,菊野亨
第2回ソフトウェア信頼性ワークショップ
447
6
3
混乱度合の変化傾向を利用した混乱プロジェクトの特徴付け
2005
松下 誠,大場 勝,肥後 芳樹,天嵜 聡介,川口 真司,水野 修,丸山 勝久
Makoto Matsushita, Masaru Ohba, Yoshiki Higo, Sousuke Amasaki, Shinji Kawaguchi, Osamu Mizuno, Katsuhisa Maruyama
情報処理学会研究報告
485
0
75, 2005-SE-149
41-48
3
第27回ソフトウェア工学国際会議(ICSE2005)参加報告
2005
2005
Sousuke Amasaki, Osamu Mizuno, Tohru Kikuno
本研究では,ソフトウェア開発プロジェクトの各工程における作業とソフト
ウェアプロダクトの最終的な品質の関係をモデル化することによる最終品質
予測モデルの構築を試みる.モデル化の手法としては,予測を行う時点にお
いて値が不確定なメトリクスをモデルに含むことが可能なベイジアンネット
を採用する.モデル化の対象はソフトウェア開発工程における残存不具合数
の推移である.具体的には,残存不具合数の推移を軸として,レビューやテ
スト作業によって発見された不具合数,それらの作業に要した工数の依存関
係を利用してモデル化を行った.そしてモデルの性能評価を行い,最終品質
予測モデルとして有用であることを示した.
天嵜聡介,水野修,菊野亨
電子情報通信学会技術研究報告
273
1
617, SS2002-40
19-24
3
ベイジアンネットに基づくソフトウェア開発工程の最終品質予測モデルの提案
102
2003
Sousuke Amasaki, Takashi Yoshitomi, Osamu Mizuno, Tohru Kikuno, Yasunari Takagi
天嵜聡介,吉富隆,水野修,菊野亨,高木徳生
電子情報通信学会技術研究報告
261
5
63, SS2002-6
31-36
3
ソフトウェア開発における不具合発見履歴と最終品質の関係に対する統計的分析
102
2002