Hideaki Hata,Osamu Mizuno,Tohru Kikuno
There have been many bug prediction models built with historical metrics, which are mined from version histories of software modules. Many studies have reported the effectiveness of these historical metrics. For prediction levels, most studies have targeted package and file levels. Prediction on a fine-grained level, which represents the method level, is required because there may be interesting results compared to coarse-grained (package and file levels) prediction. These results include good performance when considering quality assurance efforts, and new findings about the correlations between bugs and histories. However, fine-grained prediction has been a challenge because obtaining method histories from existing version control systems is a difficult problem. To tackle this problem, we have developed a fine-grained version control system for Java, Historage. With this system, we target Java software and conduct fine-grained prediction with well- known historical metrics. The results indicate that fine-grained (method-level) prediction outperforms coarse-grained (package and file levels) prediction when taking the efforts necessary to find bugs into account. Using a correlation analysis, we show that past bug information does not contribute to method-level bug prediction.
21.3%, 87/408
Hideaki Hata,Osamu Mizuno,Tohru Kikuno
Hideaki Hata,Osamu Mizuno,Tohru Kikuno
Proc. of 34th International Conference on Software Engineering (ICSE2012)
661
6
Zurich, Switzerland
200-210
1
Bug Prediction Based on Fine-grained Module Histories
2012
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
Osamu Mizuno,Tohru Kikuno,Yasunari Takagi,Keishi Sakamoto
During the process of software development, senior managers often find
indications that projects are risky and take appropriate actions to
recover them from this dangerous status. If senior managers fail to
detect such risks, it is possible that such projects may collapse
completely.
In this paper, we propose a new scheme for the characterization of
risky projects based on an evaluation by the project manager. In order
to acquire the relevant data to make such an assessment, we first
designed a questionnaire from five viewpoints within the projects:
requirements, estimations, team organization, planning capability and
project management activities. Each of these viewpoints consisted of a
number of concrete questions. We then analyzed the responses to the
questionnaires as provided by project managers by applying a logistic
regression analysis. That is, we determined the coefficients of the
logistic model from a set of the questionnaire responses. The
experimental results using actual project data in Company A
showed that 27 projects out of 32 were predicted correctly. Thus we
would expect that the proposed characterizing scheme is the first step
toward predicting which projects are risky at an early phase of the
development.
14%, 49/339
Osamu Mizuno,Tohru Kikuno,Yasunari Takagi,Keishi Sakamoto
Osamu Mizuno,Tohru Kikuno,Yasunari Takagi,Keishi Sakamoto
Proc. of 22nd International Conference on Software Engineering (ICSE2000)
119
6
Limerick, Ireland.
387-395
1
Characterization of risky projects based on project managers evaluation
2000
Osamu Mizuno,Tohru Kikuno,Katsumi Inagaki,Yasunari Takagi,Keishi Sakamoto
This paper discusses the effects of the estimation accuracy
for software development cost on both the quality of the delivered codes
and the productivity of the development team. The estimation accuracy is
measured by metric RE(called relative error). Similarly, the quality
and productivity are measured by metrics FQ(field quality) and
TP(team productivity). Using actual project data on thirty-one
projects at a certain company, the followings are verified by
correlation analysis and test of statistical hypotheses: (1) There is a
high correlation between the faithfulness of development plan to
standards and the value of RE(A coefficient of correlation between
them is -0.60). (2) Both FQ and TP are significantly different
between projects with -10%
19%, 41/209
Osamu Mizuno,Tohru Kikuno,Katsumi Inagaki,Yasunari Takagi,Keishi Sakamoto
Osamu Mizuno,Tohru Kikuno,Katsumi Inagaki,Yasunari Takagi,Keishi Sakamoto
Proc. of 20th International Conference on Software Engineering (ICSE98)
101
4
Kyoto, Japan.
410-419
1
Analyzing effects of cost estimation accuracy on quality and productivity
1998
Shinji Kusumoto,Osamu Mizuno,Tohru Kikuno,Yuji Hirayama,Yasunari Takagi,Keishi Sakamoto
In this paper, we propose a new model for software projects and an estimation method for the quality, cost and delivery date. The new model consists of Project model and Process model. Project model focuses on three key components: activity, product and developer of the project. Process model includes a set of Activig models, each of which specifies design, coding, review, test, and debug activities respectively using GSPN. Moreover, the new model can take the influence of human factors into account by introducing the concept of workload. Next, we develop a simulator which supports description of the target process, executes the process described by Activity model and analyses the simulation results statistically. Then, we apply the simulator to real software projects at certain organization and compare the estimated values with actual data. The experimental results show the applicability of the proposed simulator to manage real software project in the future.
22%, 50/219
Shinji Kusumoto,Osamu Mizuno,Tohru Kikuno,Yuji Hirayama,Yasunari Takagi,Keishi Sakamoto
Shinji Kusumoto,Osamu Mizuno,Tohru Kikuno,Yuji Hirayama,Yasunari Takagi,Keishi Sakamoto
Proc. of the 19th International Conference on Software Engineering (ICSE97)
91
5
Boston, USA.
293-303
1
A new software project simulator based on generalized stochastic Petri-net
http://doi.ieeecomputersociety.org/10.1109/ICSE.1997.610274
1997