Osaka University Applicant to ISERN Katsuro Inoue Osaka

Osaka University Applicant to ISERN Katsuro Inoue (Osaka Univ. )

Osaka University Osaka-u NAIST • 70 years history • 12 schools • 20, 000 students • SE research lab: 3 groups 9 faculty members 2

EASE Project Empirical Approach to Software Engineering

What is EASE Project? u Japan’s first large project for empirical software engineering (2003~2007: 5 years) u Government fund + industry fund u Leader: Koji Torii, NAIST with many researchers in NAIST and Osaka Univ u Laboratory at northern Osaka (Senri Lab) u 1. 5 hours from NAIST u 15 minutes from Osaka Univ. 4

Goals 1. Develop technologies and tools Effectively improve software productivity and quality n Activate software industry n 2. 3. Publish research papers Make bridge between software industry and academia 5

Classification of SE Technologies by Target Scale Our target Mega Software Engineering 6

Mega Software Engineering (MSE) Huge data collection from tens of thousands of projects n Intensive analysis of the collected data n Effective feedback for organizational benefit n 7

Example Scenario of MSE (1) Scheduled progress of project X 1 Actual progress of project X 2 E W A X Y P Find projects similar to X - Project categorization - Collaborative filtering C T Q V 8

Example Scenario of MSE (2) 3 Average reuse rate in similar projects Project X’s reuse rate - Code-clone detection 4 Promote using software asset search engine to project X - Software asset search engine 9

MSE Framework Analysis Open-source Software Data Collection Feedback Associate Organizations Software Development Organization 10

Implementation of MSE Framework n Open source development tools ¨ CVS: versioning ¨ Mail. Man: mail management ¨ Gnats: issue tracking n Existing Web interface ¨ Corporate Source （Zee. Source） 11

Standardizing MSE Data n Product data ¨ CVS format ¨ Product definition in XML n Process data ¨ Event n sequence in XML DB ¨ Postgre. SQL + XML extension 12

MSE Technologies for Intensive Analysis(1): System Classification by Similarity Measure 13

MSE Technologies for Intensive Analysis(2): Finding the Most Similar Project by Collaborative Filtering Focused Represen. Outcome Q&M Collaborative tative Adopted Resources App. A 9 9 9 7 7. 5 (target) App. B 8 7 8 ? (missing) 8 App. C ? (missing) 8 8 8 7 App. D 7 6 ? (missing) 9 6 14

MSE Technologies for Intensive Analysis(3): Fast Code Clone Detection by CCFinder 15

MSE Technologies for Intensive Analysis(4): Software Asset Search by SPARS 16

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Project Alliance International Advisories Senri Lab Core Company -Manager -NTT Software -Company engineers -Hitachi -Univ. researchers -Hitachi GP -SRA Core Univ. -NAIST -Osaka Univ. Technical Committees -Metrics -Security -Process Improvement -Formal Method … Associate Companies Associate Universities 19

Future Plan Demonstrate the first prototype of MSE framework at the workshop in Nov. 2003 n Distribute the prototype to companies in 2004 n Organize a consortium for promotion n 20

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Schedule n First 3 years: ¨ Make prototypes ¨ Evaluate the prototypes ¨ Complete distribution system n Following 2 years: ¨ Disseminate the system 23

Manager Developer System Architecture Analysis Tools(Next release) GUI (6) (6 -2) (6 -3) (6 -4) GUI Manager (1) Developer (5) Estimation Tools Similarity Tools. . . Postgre. SQL＋XML Plug-in (5 -0) XML-DB Repository Standardized Data Ruby. Script Translator 1 Translator 2 (2) (3) Option１ Log & objects CVS Mail Translator 3 (4) Option２ Other Tools (Next release) Gnats (bug-rep. ) Corporate. Source Software Development Environments 24

Java classes Raw component archive Component rank engine Internet / Organization Repositories Query handler Query/ reply Software Component Searcher Ranked component archive SPARS-J 25