Faculty Profiles - MONDEN Akito

Papers - MONDEN Akito

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ソフトウェア開発における適正工期に関する分析 Reviewed

戸田航史, 門田暁人, 松本健一

経済調査研究レビュー 7 ( 7 ) 38 - 46 2010.9

開発者メトリクスに基づくソフトウェア信頼性の分析 Reviewed

?本真佑, 亀井靖高, 門田暁人, 松本健一

電子情報通信学会論文誌 J93-D ( 8 ) 1576 - 1589 2010.8

欠損値処理法を用いた工数予測におけるデータ件数と予測精度の関係

田村晃一, 門田暁人, 松本健一

コンピュータソフトウェア 27 ( 2 ) 100 - 105 2010.5

An effect of data size on performance of effort estimation with missing data techniques

Koichi Tamura, Akito Monden, Ken-Ichi Matsumoto

Computer Software 27 ( 2 ) 100 - 105 2010.5

Language：Japanese Publishing type：Research paper (scientific journal)

To deal with missing data in historical project data sets is an important issue for constructing effort estimation models. Past researches have showed that the similarity-based imputation showed high estimation performance. However, it is unclear if it is still effective for small data sets. In this paper, using multiple data sets with different project cases each extracted from ISBSG data set, we present an experimental evaluation among four methods: mean imputation, similarity-based imputation, row-column deletion and pairwise deletion. The result showed that the row-column deletion showed better performance than similarity-based imputation for data sets not exceeding 220 cases.

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粗粒度モジュールに対するバグ密度予測の精度評価

亀井靖高, 本真佑, 門田暁人, 松本健一

電子情報通信学会技術研究報告 109 ( 456 ) 145 - 150 2010.3

Using search-based metric selection and oversampling to predict fault prone modules Reviewed

R. Vivanco, Y. Kamei, A. Monden, K. Matsumoto, D. Jin

Canadian Conference on Electrical and Computer Engineering 2010

Language：English Publishing type：Research paper (international conference proceedings)

Predictive models can be used in the detection of fault prone modules using source code metrics as inputs for the classifier. However, there exist numerous structural measures that capture different aspects of size, coupling and complexity. Identifying a metric subset that enhances the performance for the predictive objective would not only improve the model but also provide insights into the structural properties that lead to problematic modules. Another difficulty in building predictive models comes from unbalanced datasets, which are common in empirical software engineering as a majority of the modules are not likely to be faulty. Oversampling attempts to overcome this deficiency by generating new training instances from the faulty modules. We present the results of applying searchbased metric selection and oversampling to three NASA datasets. For these datasets, oversampling results in the largest improvement. Metric subset selection was able to reduce up to 52% of the metrics without decreasing the predictive performance gained with oversampling. © 2010 IEEE.

DOI： 10.1109/CCECE.2010.5575249

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原価率とプロジェクトメトリクスに着目したソフトウェア開発プロジェクトの特徴分析 Reviewed

上野秀剛, 亀井靖高, 門田暁人, 松本健一

プロジェクトマネジメント学会誌 12 ( 5 ) 25 - 30 2010

クローンメトリクスを用いたFault-Prone モジュール判別の追実験 Reviewed

亀井靖高, 左藤裕紀, 門田暁人, 川口真司, 上野秀剛, 名倉正剛, 松本健一

電子情報通信学会論文誌 93-D ( 4 ) 544 - 547 2010

Revisiting Common Bug Prediction Findings Using Effort Aware Models Reviewed

Yasutaka Kamei, Shinsuke Matsumoto, Akito Monden, Ken-ichi Matsumoto, Bram Adams, Ahmed E. Hassan

In Proceedings of The 26th IEEE International Conference on Software Maintenance (ICSM2010) 1 - 10 2010

FIT data selection based on project features for software effort estimation models

Koji Toda, Akito Monden, Ken-Ichi Matsumoto

Proceedings of the 6th IASTED International Conference on Advances in Computer Science and Engineering, ACSE 2010 82 - 88 2010

Language：English Publishing type：Research paper (international conference proceedings)

To construct a better multivariate regression model for software effort estimation, this paper proposes a method to automatically select projects as fit data (a dataset for model construction) from a given project data set based on an estimation target's features. As a result of an experimental evaluation using the ISBSG data set, which is one of the most commonly used project data sets for effort estimation studies, the proposed method showed better estimation performance than the conventional method (of constructing a model using all project data). The median of MRE (Magnitude of Relative Error) was improved from 0.552 to 0.383, and also the median of MER (Magnitude of Error Relative) was improved from 0.457 to 0.381. While regression models were often constructed using all available project data, this paper showed the necessity of fit data selection, and showed that the proposed method is one of the effective and systematic means of doing the selection.

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The effect of log transformation in multivariate liner regression models for software effort prediction

Monden, A., Kobayashi, K.

Computer Software 27 ( 4 ) 2010

Low Quality Module Prediction from Design Documents Metrics using Software Tag

Shin-ichi Katayama, Kimiharu Ohkura, Kyohei Fushida, Shinji Kawaguchi, Masataka Nagura, Akito Monden, Hajimu Iida

IEICE technical report 109 ( 343 ) 67 - 72 2009.12

Language：Japanese Publishing type：Research paper (conference, symposium, etc.) Publisher：電子情報通信学会

It is important to detect low quality modules which are likely to have many defects at an early development phase for creating high quality software. Hence, various prediction models are proposed such as linear discriminant analysis, logistic regression analysis, and neural networks. These methods, however, use metrics measured mainly from a source code, thus they can only be applied after implementation is completed. We attempt to predict low quality modules using design documents metrics included in software tags. Using design documents metrics, we can predict when design is completed, thus our method contribute to reliability enhancement activities at an implementation phase. We applied multiple regression analysis and genetic programming, and detected low quality modules with high accuracy compared to a random selection method.

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Software development productivity of Japanese enterprise applications Reviewed

Tsunoda, M., Monden, A., Yadohisa, H., Kikuchi, N., Matsumoto, K.

Information Technology and Management 10 ( 4 ) 193 - 205 2009.12

ソフトウェア開発工数予測のためのフィットデータ選定方法 Reviewed

戸田航史, 門田暁人, 松本健一

情報処理学会論文誌 50 ( 11 ) 2699 - 2709 2009.11

ソフトウェアバグと変数名の関係の分析

山本博之, 亀井靖高, 本真佑, 門田暁人, 松本健一

電子情報通信学会技術研究報告 109 ( 307 ) 67 - 71 2009.11

開発者メトリクスを用いたソフトウェア信頼性の分析 Reviewed

?本真佑, 亀井靖高, 門田暁人, 松本健一

ソフトウェア工学の基礎ワークショップ FOSE2009 35 207 - 214 2009.11

メトリクス値の標準化によるFault-Proneモジュール判別モデルの精度向上

藏本達也, ?本真佑, 亀井靖高, 門田暁人, 松本健一

情報処理学会研究報告 2009-SE-166 ( 10 ) 2009.10

ソフトウェア開発プロファイルデータの相関ルール分析 Reviewed

森崎修司, 木村早苗, 門田暁人, 松本健一

経済調査研究レビュー 5 ( 5 ) 88 - 94 2009.9

Empirical evaluation of similarity-based missing data imputation for effort estimation Reviewed

Tamura, K., Toda, K., Tsunoda, M., Monden, A., Matsumoto, K.-I., Kakimoto, T., Ohsugi, N.

Computer Software 26 ( 3 ) 44 - 55 2009.7

Modeling of Test Effort Allocation and Software Reliability in Fault-prone Module Detection Reviewed

Takeshi Kakimoto, Akito Monden, Yasutaka Kamei, Shinsuke Matsumoto, Ken-ichi Matsumoto, Shinji Kusumoto

IPSJ Journal 50 ( 7 ) 1716 - 1724 2009.7

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：一般社団法人情報処理学会

Various fault-prone detection models have been proposed to improve software reliability. However, while improvement of prediction accuracy was discussed, there was few discussion about how the models shuld be used in the field, i.e. how test effort should be allocated. Thus, improvement of software reliability by fault-prone module detection was not clear. In this paper, we proposed TEAR (Test Effort Allocation and software Reliability) model that represents the relationship among fault-prone detection, test effort allocation and software reliability. The result of simulations based on TEAR...

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