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.

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 /spl 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.

In practical software development, software quality is generally evaluated by the number of residual defects. To keep the number of residual defects within a permissible value, too much effort is often assigned to software testing. We try to develop a statistical model to determine the amount of testing effort which is needed to assure the field quality. The model explicitly includes design, review, and test (including debug) activities. Firstly, we construct a linear multiple regression model that can clarify the relationship among the number of residual defects and the efforts assigned to design, review, and test activities. We then confirm the applicability of the model by statistical analysis using actual project data. Next, we obtain an equation based on the model to determine the test effort. As parameters in the equation, the permissible number of residual defects, the design effort, and the review effort are included. Then, the equation determines the test effort that is needed to assure the permissible residual defects. Finally, we conduct an experimental evaluation using actual project data and show the usefulness of the equation.

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.

This paper discusses the effects of estimation accuracy for software development cost on both the quality of the delivered code and the productivity of the development team. The estimation accuracy is measured by metric RE (relative error). 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 following are verified by correlation analysis and testing of statistical hypotheses. There is a high correlation between the faithfulness of the development plan to standards and the value of RE (a coefficient of correlation between them is -0.60). Both FQ and TP are significantly different between projects with -10%<RE<+10% and projects with RE/spl ges/+10% (the level of significance is chosen as 0.05).

Software organizations and projects need guidance on how to improve software process, not just guidelines on what to improve. Several surveys demonstrate that the Capability Maturity Model (CMM) and ISO-9000 only provide the latter. We report our in-depth analysis on a seventeen-month effort in software process improvement (SPI) at OMRON Corporation. The goal of the analysis was to identify issues and challenges of SPI and to design a step-wise practical method to avoid such problems. Major problems we have found include the lack of shared goal among stakeholders, insufficient understanding of the current progress of SPI efforts, and underutilization of a large amount of complex information generated during SPI. We present the method for software organizations and projects for dealing with the problems, and argue for a knowledge-based SPI support system based on the method.

In order to manage software projects quantitatively, we have presented a new model far software project based on generalized stochastic Petri-net model which can take influence of human factors into account, and we have already developed software project simulator based on GSPN model. This paper proposes methods for calculating model parameters in the new model and estimating the number of faults in the design and debug phases of software process. Then we present experimental evaluation of proposed method using a data of actual software development project on a certain company. As the result of case study, we confirmed its effectiveness with respect to estimating the number of faults in the software process.

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 Activity 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.

The paper statistically analyzes the relationship between review and software quality and the relationship between review and productivity, utilizing software metrics on 36 actual projects executed in the OMRON Corporation from 1992 to 1994. Firstly, by examining the relationship between review effort and field quality (the number of faults after delivery) of each project, and the relationship between the number of faults detected in review and field quality of each project, we reasoned that: (1) greater review effort helps to increase field quality (decrease the number of faults after delivery); (2) source code review is more effective in order to increase field quality than design review; (3) if more than 10% of total design and programming effort is spent on review, one can achieve a quite stable field quality. We noticed that no relevant effects were recognized in productivity (LOC/staff month) with respect to a review rate of up to 20%. As a result of the analysis above, we recommended that 15% of review effort is a suitable percentage to use as a guideline for our software project management.