Mega Software Engineering and EASE Project

1
Mega Software Engineering and EASE Project

• Katsuro Inoue
• Osaka University

2
Overview

• Proposed a concept of Mega Software Engineering,
  which shares experiences and knowledge in
  community
• Introduced EASE project based on the concept of
  MSE
• Presented the overview of Empirical Environment
  and showed current implementation of Empirical
  Project Monitor EMP, as a partial realization of
  Empirical Environment
• Predicted ongoing directions to deeper analyses
  of empirical data

3
Empirical Software Engineering

• Various technologies in Software Engineering
  based on empirical data
• Essential for scientific improvement of project
  processes and products

4
3 Major Phases in Empirical SE
collection
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Classification of ESE Technologies by Target Scale
Mega Software Engineering
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Mega Software Engineering MSE

• Targets many projects
• A new concept but not a new technology itself
• Collection of key technologies already existing
  and emerging
• Distributed environment and data sharing
• Analysis and data mining
• Project monitoring and controlling
• Scalable computing
• ...
• Use advances of hardware performance, e.g.,
  network bandwidth, CPU clock, memory space, disk
  capacity, ...
• Software engineering technology should share in
  advances of hardware, which is mainly used for
  multimedia, grid, simulator, ...

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Characteristics of MSE

• Experience and knowledge of individual developer
  or project are collected, refined as assets, and
  reused in community
• Single-level flat static community for
  information sharing
• Automatic process Little burden is required for
  each developer or manager
• View from the organizational benefits may be
  directly obtained (no individual developers
  view or project view)
• Open source development is a simple case of MSE
  (MSE focuses analysis and feedback)

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EASE Project

• Empirical Approach to Software Engineering
• Using the concept of MSE as its basis
• Funded by MEXT (Japanese government, Ministry of
  Education, Culture, Sports, Science and
  Technology)
• 5 year project starting 2003

Senri Lab.
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Project Target
Empirical software development environment from 1
to thousands of projects
Empirical Environment
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Project Objectives

• Development of empirical environment
• Application of empirical environment to real
  projects
• Collection of data and expertise of empirical SE
• Organizational benefits by applying empirical
  environment

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Concept of Empirical Environment
Internet
Public Domain Software

Open Source Project
Collection
Improvement
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ImplementingEmpirical Environment
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(1)Policy for Collection

• Goal first (ideal cases)
• ? Data collection first (Realistic approach)
• Collect mainly product data(Obtain process data
  from product data)
• Minimize developers overhead for collection
• Raw data without human tampering
• Real-time collection
• Applicable to various projects
• Small scale
• Non-water fall process such as XP
• Distributed development including sub-contracting

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(2)Policy for Analysis
Step-wise implementation
difficult
5.
4.Reuse comp./ expertise
3.Classification and evolution
2.Inter-project metrics
simple
1.Process / product metrics inside single project
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(3)Policy for Improvement

• Feedback method for each objective
• Various mechanisms for various cases
• Currently construct a browser for visualizing
  collected data and measured metrics

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Empirical Project Monitor EPM

• A partial implementation of Empirical Environment
• Collect, measure, and show various data for
  project control
• Data source
• Versioning system CVS
• Mailing list manager Mailman
• Issue tracking tool GNATS

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Architecture of EPM
analysis tools
developer manager
measurement of intra and inter projects
PostgreSQL(Repository)
Standardized empirical SE data (in XML)
developer manager
prediction/ schedule metrics value other tool
data etc.
versioning history
mail history
problem history
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Characteristics of EPM

• Use open source development tools
• ? Easy to introduce
• Small overhead of data collection
• Most data from versioning history
• Communication through e-mail, and recoding issues
  by tracking tool
• Easy to transform other data format to the
  standardized empirical SE data format

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Application Area of EPM

• Large project
• Share project status immediately
• Reduce project management load
• Reduce risk for tampering data
• Small project
• Apply with small cost
• Apply to various projects, including XP and
  distributed development

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Features ofEmpirical Project Monitor
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EPM Analysis Tool

• Single activity view
• Source code size
• Issue resolution time
• Cumulative number of issue, number of unsolved
  problems, ...
• Multiple activity view
• Check-in and check-out
• Issue and mail
• check-in and issue

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Growth of LOC

• Progress monitoring
• Schedule v.s. actual

menu
Project EmpiriPrj
LOC
Cumulative LOC
month
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Growth of LOC(3 months)
LOC
Project EmpiriPrj
LOC
Check-in occurred
month
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Growth of LOC
Open source project nkf (character-code converter)
LOC
LOC
Check-in occurred
month
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Cumulative Issues/Unsolved Issues /Mean
Resolution Time
cumulative issues
ProjectEmpiriPrj
mean resolution days
cumulative issues
unsolved issues
mean resolution days
month
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Check-in and Check-out
check-out
ProjectEmpiriPrj
check-out
Check-in occurred
month
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CVS Log View
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Growth of Mail and Issues
cumulative mail
ProjectEmpiriPrj
cumulative mail
check-in occurred
issue raised
issue resolved
month
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Mail Log View
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Cumulative Issues and Check-in
cumulative issues
ProjectEmpiriPrj
cumulative issues
check-in occurred
month
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Future of Empirical Environment
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Extending Analysis Features

• Make deeper analysis and extract organizational
  expertise
• Find and reuse expertise easily

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Code clone detection
Component search
Metrics measurement
Project categorization
Cooperative filtering
Process data archive (XML format)
Product data archive (CVS format)
Format Translator
Format Translator
Format Translator
Format Translator
Versioning (CVS)
Mailing (Mailman)
Issue tracking (GNATS)
Other tool data
Managers
Project x
Project y
Corporate Source GUI
Project z
. . .
Developers
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Example Scenario (1)
Scheduled progress of project X
1
Actual progress of project X
2
Find projects similar to X - Project
categorization - Collaborative filtering
E
C
A
W
X
Y
V
Q
T
P
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Example Scenario (2)
3
Average reuse rate in similar projects
Project Xs reuse rate
- Code-clone detection
Promote using software asset search engine to
project X
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- Software asset search engine
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Expected Effect

• Productivity can be drastically improved by
  reusing organizational assets
• Management of assets can be easily performed
• Cost control can be precisely made relative to
  previous similar projects
• Reliability can be improved using issue history

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Analysis Technology (1) Fast Code Clone
Detection
Code clones similar portions of program
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Analysis Technology (2) System Similarity Using
Code-Clone Detecion
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Analysis Technology (3)Collaborative Filtering
Represen-tative
Focused
Collaborative
Q MResources
OutcomeAdopted
7.5 (target)
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9
9
7
App. A
8
7
8
? (missing)
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App. B
? (missing)
8
8
8
7
App. C
7
6
? (missing)
9
6
App. D
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Analysis Technology(4)
Java Class Search Engine SPARS-J
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Markov Model

• Component rank model can be considered as a
  Markov Chain of user's focus
• User's focus moves from one component to another
  along a use relation at a fixed time duration
• Node weight represents the existence probability
  of the user's focus at infinite future

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Demo of SPARS-J

• http//demo.spars.info

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Current Status and Schedule

• Current - Demo version of EPM
• First quarter of 2004
• a release of EPM
• First quarter of 2005
• Application of EPM in industry
• End of 2005
• Inclusion of analysis tools
• User group, consortium, interest group, ...

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Summary

• Proposed a concept of Mega Software Engineering,
  which shares experiences and knowledge in
  community
• Introduced EASE project based on the concept of
  MSE
• Presented the overview of Empirical Environment
  and showed current implementation of Empirical
  Project Monitor EMP, as a partial realization of
  Empirical Environment
• Predicted ongoing directions to deeper analyses
  of empirical data