A Proposal for Analysis and Prediction for Software Projects using Collaborative Filtering, In-Process Measurements and a Benchmarks Database

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON SOFTWARE PROCESS AND PRODUCT MEASUREMENT(MENSURA 2006)CÁDIZ – SPAINNOVEMBER, 6 – 8, 2006Alain Abran Reiner Dumke Mercedes Ruiz (Eds.)刊行年月日は会議開催日を参考にし

シンコウチュウ ノ プロジェクト ケイソク ト フィードバック ジッケン 二 モトヅク ケイソク データベース カツヨウ ホウシキ ノ テイアン

ソフトウェアエンジニアリング最前線2006 : 情報処理学会SEシンポジウ

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. Categories and Subject Descriptors D.2.9 [Software Engineering]: Management – Cost estimation

+49-631-6800-0

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

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

Is This Cost Estimate Reliable? -- The Relationship between Homogeneity of Analogues and Estimation Reliability

Analogy-based cost estimation provides a useful and intuitive means to support decision making in software project management. It derives a cost estimate required for completing a project from information about similar past projects, namely the analogues. While on average this method provides a relatively accurate cost estimate there remains a possibility of large estimation errors. In this paper, we empirically tested the hypothesis that “using more homogeneous analogues produces a more reliable cost estimate ” using a software engineering data repository established by the Software Engineering Center (SEC), Information-technology Promotion Agency, Japan. This testing showed that low and high homogeneity projects had a large variation in estimation reliability. For instance, the difference was 22.9 % (p = 0.021) in terms of percentage to get accurate estimates (better than Median of Magnitude of Relative Error). 1