Nahomi Kikuchi

Development of a hybrid cost estimation model in an iterative manner

Cost estimation is a very crucial field for software developing companies. The acceptance of an estimation technique is highly dependent on estimation accuracy. Often, this accuracy is only determined after an initial application. Possible further steps for improving the underlying estimation model typically do not influence the decision on whether to discard the technique or deploy it. In addition, most estimation techniques do not explicitly support the evolution of the underlying estimation model in an iterative manner. This increases the risk of overlooking some important cost drivers or data inconsistencies. This paper presents an enhanced process for developing a CoBRA® cost estimation model by systematically including iterative analysis and feedback cycles, and its evaluation in a software development unit of Oki Electric Industry Co., Ltd., Japan. During the model improvement cycles, estimation accuracy was improved from an initial 120% down to 14%. In addition, lessons learned with the iterative development approach are described.

Estimation of project success using Bayesian classifier

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.

The use of simulation techniques for hybrid software cost estimation and risk analysis

Abstract Cost estimation is a crucial field for companies developing software or software‐intensive systems. Besides point estimates, effective project management also requires information about cost‐related project risks, for example, a probability distribution of project costs. One possibility to provide such information is the application of Monte Carlo simulation. However, it is not clear whether other simulation techniques exist that are more accurate or efficient when applied in this context. We investigate this question with CoBRA®, 1 a cost estimation method that applies simulation, that is, random sampling, for cost estimation. This chapter presents an empirical study, which evaluates selected sampling techniques employed within the CoBRA® method. One result of this study is that the usage of Latin Hypercube sampling can improve average simulation accuracy by 60% and efficiency by 77%. Moreover, analytical solutions are compared with sampling methods, and related work, limitations of the study, and future research directions are described. In addition, the chapter presents a comprehensive overview and comparison of existing software effort estimation methods.

Software development productivity of Japanese enterprise applications

To clarify the relationship between software development productivity and the attributes of a software project, such as business area, programming language and team size, this paper analyzed 211 enterprise application development projects in Japan using a software engineering data repository established by the Software Engineering Center (SEC), Information-Technology Promotion Agency, Japan. In the analysis, we first identified factors that related to productivity based on a parallel coordinate plot (PCP) and a one-way ANOVA. An in-depth analysis on each productivity factor was then conducted by selecting a project subset for each factor so that the effect of other factors is minimized. Our findings include that the average team size was the strongest attribute relating to productivity. The outsourcing ratio (percentage), which can be controlled by software development companies, and the business sector both showed a moderate relationship to productivity. Finally, product size (FP), the duration of development and the programming language were only weakly related to productivity.

Productivity analysis of Japanese enterprise software development projects

To clarify the relation between controllable attributes of a software development and its productivity, this paper experimentally analyzed a software project repository (SEC repository), consisting of 253 enterprise software development projects in Japanese companies, established by Software Engineering Center (SEC), Information-technology Promotion Agency, Japan. In the experiment, as controllable attributes, we focused on the outsourcing ratio of a software project, defined as an effort outsourced to subcontract companies divided by a whole development effort, and on the effort allocation balance among development phases. Our major findings include both larger outsourcing ratio and smaller upstream process effort leads to worse productivity.

Improving the testing process by program static analysis

This paper describes a test process improvement aiming to improve software quality in a large organization that has a large number of software projects. First, we identified activities in the testing process in the organization and analyzed their characteristics. As a result, we identified that dynamic tests have been performed well and static tests have been less performed. Improvement plan was requested that contributes to the product quality without increasing development efforts for the projects. We then decided a plan to introduce static analysis tools and establish the testing process in which static analysis tools are applied as much as possible. Implementation of the improvement plan consists of two steps: introductory and complete application of tools to the organization. The characteristics of this process improvement are not only that tools have been evaluated and confirmed in pilot projects before actual introduction but also procedures have been designed carefully for the application of the tools to projects. The effectiveness of this approach was confirmed in the analysis of applied projects in which static problems were removed successfully before system test.

Identifying key attributes of projects that affect the field quality of communication software

The authors identify key attributes of projects in which the number of problem reports after release is remarkable in the communication software. After several interviews with software project managers, we derived candidate attributes of importance to the projects. To find out the most influential attributes of the projects, we conducted statistical analysis using a set of metrics data related to the candidate attributes and the number of problem reports after release. As a result we successfully found that two metrics concerning the origins quality and the changes in specification are useful to estimate the field quality.

Lessons Learned and Results from Applying Data-Driven Cost Estimation to Industrial Data Sets

The increasing availability of cost-relevant data in industry allows companies to apply data-intensive estimation methods. However, available data are often inconsistent, invalid, or incomplete, so that most of the existing data-intensive estimation methods cannot be applied. Only few estimation methods can deal with imperfect data to a certain extent (e.g., optimized set reduction, OSR). Results from evaluating these methods in practical environments are rare. This article describes a case study on the application of OSR at Toshiba information systems (Japan) corporation. An important result of the case study is that estimation accuracy significantly varies with the data sets used and the way of preprocessing these data. The study supports current results in the area of quantitative cost estimation and clearly illustrates typical problems. Experiences, lessons learned, and recommendations with respect to data preprocessing and data-intensive cost estimation in general are presented.