Strategy for EASE Project Kenichi Matsumoto Nara Institute of Science and Technology (NAIST) EASE Project, Ministry of Education, Culture, Sports, Science.

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Presentation transcript:

Strategy for EASE Project Kenichi Matsumoto Nara Institute of Science and Technology (NAIST) EASE Project, Ministry of Education, Culture, Sports, Science and Technology (MEXT) Japan

EASE Project 2 Strategy Successful formation of an enterprise depends on having a clear goal or vision. It needs to consider the point of view of the entire situation or environment. Basic principles or performance plan must be designed. Finally, practical priorities must be decided. 1.Goal (Vision) 2.Situation (Environment) 3.Basic Principles (Performance Plan) 4.Priorities

EASE Project 3 １． Goal To improve software productivity and reliability through increased collaboration among academia, industry, and government on using an empirical process (data collection, analysis, and feedback) Demonstrate successful practices of supporting problem- solving process in software development project by using empirical data. Popularize empirical data collection, analysis, and feedback tools in software development project.

EASE Project 4 ２． Situation Poor Quantitative Management Only about 10% of software projects in Japan are managed quantitatively. Four Types of Software Company for Empirical Approach About 30% of software company in Japan are very Interested in empirical approach, but they have no experience or tools. J-SEC (Japan Software Engineering Center) Will open in October 2004 supported by METI, Japan. Closed community Most empirical data, even statistics, which are collected by software companies have not been opened to the public.

EASE Project 5 Poor Quantitative Management in Software Development Project in Japan Nikkei Computer Report (2003/11/17)

EASE Project 6 Four types of Software Company for Empirical Approach Type 1Not Interested.6% Type 2Interested, but no experience or tools.30% Type 3 Collecting the project data by hand or tool, but analysis and feedback are missing. 36% Type 4 Collecting the project data by hand or tool, and conducting analysis and feedback. Unknown28% Feedback from participants of EASE Forum 2004 (2003/11/7)

EASE Project 7 Japan Software Engineering Center Will open in October 2004 supported by Ministry of Economy, Trade and Industry (METI). Conduct in-depth practical studies to solve the issues of today’s software industry. Software Process Improvement methods for the Japanese Industry Software measurement standards Demonstration of the methods and tools in advanced software development projects Budget of 2004: 1.48 billion yen

EASE Project 8 ３． Basic Principles Target Industry, mainly Type 2 software company. To support the problem-solving process in software development with empirical data. Approach Automatic data collection. Exploratory data analysis and feedback. Data sharing with anonymity. Promotion Demonstrate successful practices. Publish tools as open source. Use J-SEC channel to reach business world.

EASE Project Target Type 2 Company They are interested in empirical approach, but have no experience or tools for data collection, analysis, and feedback. To support the problem-solving process in software development project. Software development project = “software production process” + “problem-solving process.” Adapting “front-loading” technique from car development to software development.

EASE Project 10 Problems in Software Development Project Defects Deviation from Plan (cost overrun, time overrun,…) Low productivity, backtracking, no corrective action,… Poor communication among stakeholders Morale hazard …

EASE Project 11 Front-Loading in Problem-Solving Development Time Frontloading (Process Improvement) S. Thomke and T. Fujimoto, The effect of "front-loading" problem-solving on product development performance, Journal of Product Innovation Management, Vol. 17, No. 2, pp (2000). Corrective Action Proposal Curve Problem Fixing Curve Problem Detection Curve

EASE Project 12 Front-Loading by Data Analysis Problem Detection Curve Corrective Action Proposal Curve Problem Fixing Curve Frontloading (Process Improvement) Project Browsing Project Previewing Spectrum Metrics Code Clone Analysis Process Pattern Analysis … Project Simulation Similarity Analysis Classification Tree … Report Analysis … Development Time

EASE Project Approach Automatic data collection. Exploratory data analysis and feedback. Data sharing with anonymity.

EASE Project 14 Automatic Data Collection Tool-based data collection with negligible workload for software developers. Data collection tool is common as far as possible. Common data format. Use open source software for front-end.

EASE Project 15 Exploratory Data Analysis and Feedback Clarify (tacit) knowledge of problem-solving in software development through interviews and discussion with individual project managers. Provide exploratory functions to the collected data and open interfaces to other project management tools, in order to meet various needs of project managers. Project Browser (Monitor, Spectrum metrics, …) Project Previewer (Predictor, Simulator, …) Data integration with project plan (WBS,…) Data exchange with other tools (ex. With COCOMO II)

EASE Project 16 Data Sharing with Anonymity Be careful about unnecessarily centralizing collection of data. (Company is not requested to open his/her empirical data.) Develop theories and tools for controlling and securing anonymity of empirical data in sharing among software companies and academia.

EASE Project 17 Steps to Goal Step 1: Develop Data Collection tool (EPM) Step 2: Apply EPM to “Type 2” companies. 7 companies are now applying EPM to their projects. Step 3: Explore collected data to make analysis and feedback functions clear. Data of Type 3+4 companies will be available from J-SEC. Step 4: Demonstrate successful practices in Step 3. Step 5: Develop EPM with data analysis and feedback functions. Step 6: Develop EPM with function for controlling and securing anonymity on empirical data sharing.

EASE Project Promotion Demonstrate successful practices for supporting problem-solving process in software development project by using empirical data. Publish data collection, analysis, and feedback tools as open source. Popularize successful practices and tools through J- SEC channel to business world.

EASE Project 19 ４． Priority 1.Demonstrate successful practices. 2.Develop data collection tool as open source. 3.Develop data analysis and feedback tool as open source. 4.Develop theories and tools for controlling and securing anonymity of empirical data in sharing among software companies and academia.