Software Engineering Research

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Software Engineering Research Developmentat
A.U.Th

• Ioannis Stamelos
• Panagiotis Katsaros
• stamelos, katsaros_at_csd.auth.gr

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Presentation Outline

• Introduction
• AUTH / Dept of Informatics
• Software Engineering Research Group
• Software Quality, Management, Education
• Open Source Software Engineering / Extreme
  Programming

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Introduction
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City of Thessaloniki

• Capital of Macedonia region, northern Greece
• 1 M inhabitants, 2nd greek city, major port of
  Balkans
• 2300 years of continuous history, joined modern
  Greece in 1912

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AUTH / Dept of Informatics
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Aristotle University of Thessaloniki, Greece

• First school (Philosophy) established in 1925
• Largest Greek University
• 45 departments
• gt 60.000 students
• gt 3.000 staff / teachers
• www.auth.gr

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Department of Informatics

• School of Physical Science, plans to move to
  Polytechnic School
• Established in 1993
• 22 academic staff members
• 700 students
• www.csd.auth.gr

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4-years Degree Curriculum

• 8 semesters
• 5 semesters with obligatory courses
• 3 semesters with options out of 4 directions
• Information Systems
• Digital Media
• Multimedia / Educational Technologies
• Networks / Architecture / Communications
• Thesis Project

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Master Courses

• MSc in Informatics (4 directions)
• MSc in Informatics Management (with Dept. of
  Business Management)
• Plus
• MSc in Medical Informatics

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Five Research Labs

• Programming Languages and Software Engineering
  PLASE
• Data Technologies and Engineering -DELAB
• Information Processing and Artificial
  Intelligence
• Multimedia (Educational Technologies)
• Networks, Communications and Architecture

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Programming Languages and Software Engineering
PLASE

• 6 staff members
• 10 research associates
• 20 PhD students
• Major research areas
• Intelligent Systems
• Web Engineering
• Software Engineering
• Mathematics

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Software Engineering Group (SWENG)

• 3 ac. staff members
• Assistant Professors I. Stamelos, L. Angelis /
  PLASE
• P. Katsaros / DELAB
• 5 research associates
• 10 PhD students (joined the group during last 4
  years)
• http//sweng.csd.auth.gr

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SWENG Teaching

• Dept. of Informatics / Hellenic Open University
  (ODL)
• Mathematics, Graph Theory
• Language Theory, Compilers
• Introduction to Software Engineering
• Object Oriented Analysis / Java / C
• Master Courses
• Software Project Management
• Enterprise Information Systems

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SWENG RD Activities

• Basic Research
• Collaboration with other research teams in Greece
  and in EU
• Politecnico di Torino (Prof. Morisio)
• Univ. LAMSADE / Paris (Prof. Tsoukias)
• Univ. Juan Carlos / Madrid (Prof. Barahona)
• Funded Applied Research Development
• Greek Public Sector
• Greek Secretariat for Research and Technology
• Private Companies
• EU

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SWENG Funding

• Software industry context
• Public Sector still developing IT infrastructure
  (e.g. transportation)
• Private Sector companies enhancing IT
  infrastructure (banks, utilities, privatized
  public companies)
• Most large SW Companies mainly acting as vendors
  (e.g. of ERP systems)
• Various small but aggressive SW companies acting
  as developers
• Funding limited but growing fast
• (in last 5 years, while SWENG group was growing)
  0,7 M euros
• mostly from development projects
• participated also to other PLASE projects
  (MATHIND, Go Net, )
• Now shifting to research projects as well
• obtained 0,5 M, expecting another 0,6 M in
  years 2006 - 2007 (mainly from EU, GSRT)
• see forthcoming research projects at Forthcoming
  Research Projects slide

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Examples of current / completed projects

• Expert System for Software Evaluation
• Automated Certification System (with data mining
  functionality)
• Enterprise Knowledge Management System
• Specification of various public company
  information systems (e.g. ticketing)

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SWENG Major Research Areas

• Software Quality
• Dependability, Security
• Software Cost Estimation
• Software Evaluation
• Open Source Software
• Extreme Programming
• Software Education

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Software Quality, Management, Education
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Software Quality (1)

• Problems
• how to quantify and predict / assess SW quality
  attributes
• many SW quality metrics /methods have been
  proposed but have not been sufficiently validated
• Need for
• novel, intuitively confirmed measures
• empirical evidence for assessing methods, tools,
  approaches

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Software Quality (2)

• Formal experiments
• Most powerful empirical investigation tool,
  producing generic conclusions
• A typical formal experiment setting
• Two groups of subjects are given the same object,
  differing only in the controlled variable (e.g.
  design of the same system based on two different
  design approaches, see next two slides)
• Subjects are asked to perform the same task
• All other potentially affecting variables are
  kept constant (e.g. group experience)
• The two groups performance is monitored
  accurately and statistical tests are run to
  detect statistical difference

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Software Quality (3)

• Currently investigating quality assessment based
  on Heuristics
• - A Heuristic is a good practice (e.g. avoidance
  of God classes in OO design, see previous
  slide)
• Developed tools assessing compliance with Riels
  set of object-oriented heuristics (based on
  C/Java code, UML class diagrams)
• Design of new methods / models for quality
  prediction
• Software Defect Prediction Using Regression via
  Classification (AICSSA 06)

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Decision tree for defect prediction (for a
specific data set)
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Software Cost Estimation

• The problem predict the human effort for
  developing a system
• Expert judgment
• Analogy based method, project is compared with
  past projects, project cost is proportional to
  cost of similar projects
• Algorithmic / ad hoc models, a set of equations
  are used (e.g. the COCOMO models)
• Issues
• Wrong, inaccurate, missing past projects data
• Uncertainty about new project
• Many categorical values (e.g. type of system,
  language used)
• Method accuracy
• Calibration with local data
• Method suitability to local situation (e.g. local
  project data may be not available or projects are
  not measured accordingly)

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Software Cost Estimation (1)

• Interval estimation
• A range of values is produced
• More realistic approach w.r.t to a point estimate
• Interval incorporates model error
• Interval accounts for estimation data inaccuracy
• May be easily transformed to a point estimate
  (e.g. take the mean value of the interval)
• A number of techniques has been proposed

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Software Cost Estimation (2)

• Improvement of Analogy Based Estimation
  (Bootstrap sampling method)
• To calibrate method parameters (e.g. number of
  similar projects used for the estimation)
• To produce interval estimates
• Project portfolio estimation
• Combination of interval estimates from portfolio
  project members
• Categorical, multinomial, ordinal regression
• Regression models that provide interval estimates
• Intervals are often predefined by managers
• Produced more accurate results than previously
  proposed methods (e.g. OLS)

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BRACE a tool for advanced estimation by analogy

• Screenshot of BRACE showing the results of a
  calibration session

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BRACE interval estimate generation

• New project is estimated to demand an effort of
  2000-2500 mh (prob. 65,1), 2500-3000 (prob.
  23,1), etc.

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Software Cost Estimation (3)

• How to deal with missing data in your historical
  data base?
• Imputation methods replace missing values
• Proposed Multiple Logistic Regression as an
  imputation method for missing categorical values
• MLR found to perform better w.r.t. to previously
  proposed methods

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Software Cost Estimation (4)

• Comparative assessment of proposed methods
  (focusing on AI methods)
• Association Rules, BBN, CART
• AR are found to be the most accurate / promising
  approach

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Example Association Rule for Productivity
Estimation

• Association Rule
• Support Confidence Rule Body Rule Head
• 6.3 66.6 ACAP_HDATA_N gt PROD_4
• Interpreted as follows
• When the programmers analysis capability is
  high and the database size is nominal then the
  productivity is likely to be in the fourth
  category (100ltPRODlt160 KLOC/MM)

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Software process modeling with iterative Bayesian
Belief Networks
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Software Evaluation

• Application of MCDA (Multiple Criteria Decision
  Aid) on software problem situations

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Profile evaluation of software artifacts /
processes

• User defines profiles as sets of attribute
  values, then product / process is assessed
  through the Electre method to decide its profile
  value

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Software Engineering Education

• Investigation of novel approaches in SE education
  through formal experiments
• Distance learning of UML (sequence diagrams)
• Use of a lesson sheet for teaching Usability

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Open Source Software Engineering / Extreme
Programming
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Free / Open Source Software (1)

• Revolutionary software development
• Issues
• What is the quality of F/OSS products?
• How can we assess an F/OSS project?
• Is it active?
• Is it interesting?
• How can we predict an F/OSS project?
• How will it grow in terms of code size, number of
  programmers?

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Free / Open Source Software (2)

• F/OSS product quality assessment
• F/OSS process modeling
• Dynamic models for predicting F/OSS project
  behavior
• Knowledge sharing and learning in F/OSS

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F/OSS product quality assessment

• Case Studies
• Measured and assessed 100 LINUX applications
  applications code found to be of at least equal
  quality w.r.t. to closed source code
• Measured and assessed specific software systems
  quality (e.g. assessed COMPIERE code, an Open
  Source ERP system, found to be of excellent
  quality)
• Quality assessment horizontal studies
• Measured, assessed 5 F/OSS projects and compared
  with closed source counterparts using
  Maintainability Index (SEI)
• F/OSS systems found to exhibit superior
  maintainability, however F/OSS maintainability
  deteriorated in similar way as in closed source

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F/OSS maintainability evolution
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F/OSS process modeling

• Dynamical F/OSS process model
• Based on programmer motivation
• Elaborates on programmer tasks (coding,
  debugging), modules, defects
• Calibrated on typical F/OSS projects (e.g.
  Apache, Gnome)
• Predicting curves for size (LOCs), number of
  programmers

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Knowledge sharing and learning in F/OSS

• Modeling of knowledge sharing in F/OSS
• Knowledge sharing model
• Mining F/OSS mail lists to determine knowledge
  brokers
• Learning mechanisms in F/OSS
• Learning in F/OSS occurs following the
  constructivist model
• Ultimate goal use of F/OSS projects in SE
  teaching
• Ran a pilot study 15 students practiced software
  testing by participating in F/OSS projects,
  obtained promising results

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F/OSS Knowledge Sharing Model

• Knowledge is generated, shared and acquired by
  traversing various paths of the graph model in
  many different ways

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F/OSS Mailing List Social Network
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Extreme Programming

• Surveying Agile Methods / XP spread in Greece
• SW SMEs use XP methods without knowing it!
• Investigation of human factors
• Developer personality and temperament when
  forming a pair in pair programming
• Conducted formal experiments with students
• Found that mixed personalities and temperaments
  produce better results

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Forthcoming Funded Research Projects

• SQO OSS (IST/EU), Software quality observatory
  for Open Source, a system that will continuously
  monitor and evaluate OSS products
• FLOSSMETRICS (IST/EU), studies and models for OSS
  process management
• F/OSS in education (MINERVA / EU), exploitation
  of OSS in informatics education
• DIERGASIA (means PROCESS in Greek) (TELETEL /
  GSRT), prediction models and procedures for
  advanced software quality assurance in a TLC
  systems company
• Also bidding for funding for various development
  projects (training in basic computer skills using
  ODL, portal development for technology museums,
  )

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Current / Future Research topics

• F/OSS quality assessment through the use of
  heuristics
• Social Networks in F/OSS
• Indicators for community decay in F/OSS projects
  when is the project endangered because of mentors
  / key developers abandonment?
• Software Architecture
• Exploit architectural tactics in component
  specification to support reusability (ICSR06,
  Torino)
• Management Anti-Patterns
• Modeling with BBN
• XP management anti-patterns
• Intellectual Software Capital Valuation
• How much is a software artifact (component,
  method, piece of knowledge) worth?
• Investigate XP through experiments with
  professionals
• Introduction of F/OSS projects in SE education

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Tactic driven process