Masanari Kondo,Osamu Mizuno,Eun-Hye Choi
Software effort estimation is a critical task for successful software development, which is necessary for appropriately managing software task assignment and schedule and consequently producing high-quality software. Function point (FP) metrics are commonly used for software effort estimation. To build a good effort estimation model, independent explanatory variables corresponding to FP metrics are required to avoid a multicollinearity problem. For this reason, previous studies have tackled analyzing correlation relationships between FP metrics. However, previous results on the relationships have some inconsistencies. To obtain evidence for such inconsistent results and achieve more effective effort estimation, we propose a novel analysis, which investigates causal-effect relationships between FP metrics and effort. We use an advanced linear non-Gaussian acyclic model called BayesLiNGAM for our causal-effect analysis, and compare the correlation relationships with the causal-effect relationships between FP metrics. In this paper, we report several new findings including the most effective FP metric for effort estimation investigated by our analysis using two datasets.
Masanari Kondo,Osamu Mizuno,Eun-Hye Choi
Masanari Kondo,Osamu Mizuno,Eun-Hye Choi
758
International Journal of Mathematical, Engineering and Management Sciences (IJMEMS)
0
ISSN: 2455-7749
2
90–112
0
Causal-Effect Analysis using Bayesian LiNGAM Comparing with Correlation Analysis in Function Point Metrics and Effort
http://www.ijmems.in/assets/8-vol.-3%2C-no.-2%2C-90–112%2C-2018.pdf
3
2018
Osamu Mizuno,Seiya Abe,Tohru Kikuno
我々はこれまでロジスティック回帰分析に基づくプロジェクト混乱予測を行っ
てきた.本研究ではロジスティック回帰分析におけるいくつかの問題を解決
するために,ベイズ識別器を利用した混乱予測手法を提案する.まずベイズ
識別器に基づく6つのデータマイニング手法に対して精度比較実験を行い,
最も精度の高い手法を選択し,採用している.次に,提案法の有効性を実際
の開発現場から得られたデータに適用することで確認した.具体的にはロジ
スティック回帰分析では混乱予測を誤ったプロジェクトについても正しい予
測結果を得ることができた.
水野 修,安部 誠也,菊野 亨
Osamu Mizuno,Seiya Abe,Tohru Kikuno
448
SEC journal
SEC journal
11
優秀論文賞受賞
4
24-35
0
プロジェクト混乱予測システムのベイズ識別器を利用した開発
Development of Project Confusion Predicting System Using Bayesian Classifier Towards its Application to Actual Software Development
http://ssl.ohmsha.co.jp/cgi-bin/menu.cgi?ISBN=4-SEC-00004-0
1
2005
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
Eun-Hye Choi,Tsuyoshi Fujiwara,Osamu Mizuno
Eun-Hye Choi,Tsuyoshi Fujiwara,Osamu Mizuno
Eun-Hye Choi,Tsuyoshi Fujiwara,Osamu Mizuno
Proc. of 10th IEEE International Conference on Software Testing Verification and Validation Workshop (ICST2017), Posters track
748
3
189-191
1
Weighting for Combinatorial Testing by Bayesian Inference
2017
Masanari Kondo,Osamu Mizuno
In the effort estimation studies, we can obtain open datasets from the past research. Those datasets are either within- company or cross-company dataset. On effort estimation, it was long discussed which dataset is appropriate for building accurate model. To find a new viewpoint in this discussion, we introduce the causal-effect relationship estimation technique. We use a simple Bayesian approach that is defined by the data generation model in a Linear Non-Gaussian Acyclic Model( LiNGAM ). This model is applied to the function point and effort metrics in both within-company and cross-company datasets. We assume that if a dataset is appropriate for effort estimation, causal-effect relationships between metrics and effort will be extracted more. The result of case study shows that we can extract more causal- effect relationships from the cross-company dataset than that of from the within-company dataset.
Masanari Kondo,Osamu Mizuno
Masanari Kondo,Osamu Mizuno
Proc. of 27th International Symposium on Software Reliability Engineering (ISSRE2016), Workshops proceeding
736
10
Ottawa, Canada
47-48
1
Analysis on Causal-Effect Relationship in Effort Metrics Using {Bayesian} {LiNGAM}
10.1109/ISSREW.2016.18
2016
Seiya Abe,Osamu Mizuno,Tohru Kikuno,Nahomi Kikuchi,Masayuki Hirayama
The software projects are considered to be successful if the
cost and the duration are within the estimated ones and the
quality is satisfactory. To attain project success, the project
management, in which the final status of project is estimated,
must be incorporated.
In this paper, we consider estimation of the final status(that
is, successful or unsuccessful) of project by applying Bayesian
classifier to metrics data collected from project. In order to
attain high estimation accuracy rate, we must select only a
set of appropriate metrics to be applied. Here we consider two
selection methods: the first method by the experts and the second
method by the statistical test.
Then we conducted an experiment using 28 project data and 29
metrics data in an organization of a certain company. The result
showed that the method by the test gave higher accuracy rates than
the method by the experts, and Bayesian classifier with the test method is
effective to estimate project success.
Seiya Abe,Osamu Mizuno,Tohru Kikuno,Nahomi Kikuchi,Masayuki Hirayama
Seiya Abe,Osamu Mizuno,Tohru Kikuno,Nahomi Kikuchi,Masayuki Hirayama
Proc. of 28th International Conference on Software Engineering (ICSE2006)
466
5
Shanghai, China
600-603
1
Estimation of Project Success Using Bayesian Classifier
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
Seiya Abe,Osamu Mizuno,Tohru Kikuno
In order to realize reliable software development, prob- lems in software development must be detected and avoided as soon as possible. Thus, detecting signs of problems at an early stage of the software project is important. Much research has been carried out on the detection of problem signs of software development projects [2, 3]. This study shows an easy-to-use approach to predict runaway projects in an organization to achieve reliable development process. Experimental results of predicting runaway projects are also shown.
Seiya Abe,Osamu Mizuno,Tohru Kikuno
Seiya Abe,Osamu Mizuno,Tohru Kikuno
Proc. of 15th International Symposium on Software Reliability Engineering (ISSRE2004), Supplemental proceedings
437
11
Saint-Malo, France
23-24
1
Predicting Runaway Projects for Reliable Software Developments Using {Bayesian} Classifier
2004
Osamu Mizuno,Takanari Hamasaki,Yasunari Takagi,Tohru Kikuno
Since software development projects often fall into runaway
situations, detecting signs of runaway status in early stage of
development has become important. In this paper, we propose a new
scheme for the prediction of runaway projects based on an empirical
questionnaire. We first design a questionnaire from five viewpoints
within the projects: requirements, estimations, planning, team
organization, and project management activities. Each of these
viewpoints consists of questions in which experience and knowledge
of software risks are included. Secondly, we classify projects into
``runaway'' and ``success'' using resultant metrics data. We
then analyze the relationship between responses to the questionnaire
and the runaway status of projects by the Bayesian
classification. The experimental result using actual project data
shows that 33 out of 40 projects were predicted correctly. As a
result, we confirm that the prediction of runaway projects is
successful.
Osamu Mizuno,Takanari Hamasaki,Yasunari Takagi,Tohru Kikuno
Osamu Mizuno,Takanari Hamasaki,Yasunari Takagi,Tohru Kikuno
Proc. of 5th International Conference on Product Focused Software Process Improvement (PROFES2004)
417
4
Nara, Japan
263-273
1
An Empirical Evaluation of Predicting Runaway Software Projects Using {Bayesian} Classification
LNCS 3009
2004
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
Takahiro Kondo,Seiya Abe,Osamu Mizuno,Tohru Kikuno
近堂 高広,安部 誠也,水野 修,菊野 亨
Takahiro Kondo,Seiya Abe,Osamu Mizuno,Tohru Kikuno
情報処理学会第155回ソフトウェア工学研究会
IPSJ SIGSE Technical Report
503
3
東京
33, 2007-SE-155
57-64
3
ベイズ識別器による混乱予測に基づくソフトウェアプロジェクト管理支援ツールの試作
A prototype of software project management tool based on runaway prediction using Bayesian classifier
2007
2007
Tsuyoshi Fujiwara
藤原 剛史
Tsuyoshi Fujiwara
751
2
7
京都工芸繊維大学 大学院工芸科学研究科
ベイズ推定による優先度付き組み合わせテストの改良と不具合発見傾向の評価
Improvement of the Bayesian Inference Based Prioritized Combinatorial Testing and Assessment of the Tendency to Detect Faults
2017