SoftLab Bogazii University Department of Computer Engineering Software Engineering Research Lab http

1
SoftLabBogaziçi University Department of
Computer EngineeringSoftware Engineering
Research Labhttp//softlab.boun.edu.tr/
2
Contents

• Department of Computer Engineering
• Undergrad education
• Research Labs
• SoftLab
• Background
• Research Areas and Sample Work
• Industry Funded Projects

3
Department of Computer Engineering

• Established in 1981. First graduates in 1986.
• Alumni
• Undergrad (1050 people),
• Masters ( 200 people)
• PhD (20 people)
• MS degree in Software Engineering since 2003 (50
  people)
• Goals
• To educate the best computer engineers/ software
  engineers who could compete globally
• Excel in research
• Engage in government/ private sector funded joint
  research projects
• Establish international research collaborations

4
Undergrad Education

• Global and national undergrad curriculum
• ACM/IEEE curriculum
• Accredited by ABET since 1998
• Entry quota 50 students
• Number 1 ranked in national university entrance
  exams
• Gets from the first 300.

5
Undergrad Education

• The best instructor per student ratio 218 PhDs
  / 50 students
• Project based, teamwork driven, research and
  innovation oriented education philosophy
• Interdisciplinary and flexible, very rich
  selection of electives
• Roboust computer engineering education that
  fosters independent thinking and learning.

6
Teaching and Research Staff

• 21 full-time PhDs,
• 5 part-time PhDs,
• 20 research assistants, 9 admin staff
• TÜBITAK, DPT, FP7 and industry funded 30
  full-time graduate students and research
  assistants

7
Strong Research Labs

• 200 Graduate (130 MS, 70 PhD) and 50 undergrads
  who work on project basis, 9 Research Labs,35
  funded research projects
• 2-2,5 M annual funding

Bogaziçi Üniversitesi Bilgisayar Mühendisligi
Bölümü
8
AILAB

• Artificial Intelligence Research Lab
• 2005 world champion, 2006 Robocup first 8!
• Scored a goal to Microsoft team...

9
NETLAB

• Computer Networks Research Lab
• High speed communication and networks
• Wireless and mobile networks, cognitive radio
  networks and sattelite networks
• Sensor Networks
• Network security
• Performance of networks
• Grid computing http//netlab.boun.edu.tr

10
PILAB

• Perceptual Intelligence Research Lab
• Human-computer interaction
• Face recognition
• Hand movements
• 3-D modelling
• Voice to text/ text to voice
• Biometrics applications
• Machine Learning and Data Mining

11
SOFTLAB

• Software Engineering Research Lab
• Software Quality and Processes
• Defect prediction and cost estimation
• Code metrics
• Process Models
• Quality Standards in Embedded Systems
• Value Based SE
• SOA
• Semantic Web Services Matching
• Mobile Web Services
• Industry Collaboration
• Training, Consultancy
• Data Sharing, Modelling

12
Other Research Labs

• MEDIALAB
• Multimedia
• EDALAB
• Embedded Systems
• CASLAB
• Computer Systems Architecture
• SOSLAB
• Complex Systems

13
Software Enginering Challenges

• Heterogenous and distributed systems
• Complex systems
• Standards
• Integration
• Resuse

SOA and Web Services
Software Quality - processes
14
Softlab Research Areas

• Software Measurement
• Defect Prediction/ Estimation
• Effort Cost Estimation
• Value Based Software Engineering
• Process Improvement (CMM)
• Service Oriented Architecture/ Computing and Web
  Services

15
Code Metrics

• What do metrics show?
• Cost estimation
• Quality evaluation and improvement
• What needs to be measured?
• Which metrics to collect?
• Process metrics.
• Product metrics Static code metrics and defect
  metrics
• Which metrics? vs. How they should be used?

16
Problem 1

• How to tell if the project is on schedule and
  within budget?
• Earned-value charts.

17
Problem 2

• How hard will it be for another organization to
  maintain this software?
• McCabe Complexity

18
Problem 3

• How to tell when the subsystems are ready to be
  integrated
• Defect Density Metrics.

19
Problem Definition

• Software development lifecycle
• Requirements
• Design
• Development
• Test (Takes 50 of overall time)
• Detect and correct defects before delivering
  software.
• Test strategies
• Expert judgment
• Manual code reviews
• Oracles/ Predictors as secondary tools

20
Defect Prediction

• 2-Class Classification Problem.
• Non-defective
• If error 0
• Defective
• If error gt 0
• 2 things needed
• Raw data Source code
• Software Metrics -gt Static Code Attributes

21
Defect Prediction

• Machine Learning based models.
• Defect density estimation
• Regression models error pronness
• First classification then regression
• Defect prediction between versions
• Defect prediction for embedded systems

• Software Defect Identification Using Machine
  Learning Techniques, E. Ceylan, O. Kutlubay, A.
  Bener, EUROMICRO SEAA, Dubrovnik, Croatia, August
  28th - September 1st, 2006
• "Mining Software Data", B. Turhan and O.
  Kutlubay, Data Mining and Business Intelligence
  Workshop in ICDE'07 , Istanbul, April 2007
• "A Two-Step Model for Defect Density Estimation",
  O. Kutlubay, B. Turhan and A. Bener, EUROMICRO
  SEAA, Lübeck, Germany, August 2007
• Defect Prediction for Embedded Software, A.D.
  Oral and A. Bener, ISCIS 2007, Ankara, November
  2007
• "A Defect Prediction Method for Software
  Versioning", Y. Kastro and A. Bener, Software
  Quality Journal (in print).
• Ensemble of Defect Predictors An Industrial
  Application in Embedded Systems Domain. Tosun,
  A., Turhan, B., Bener, A. A, and Ulgur, N.I.,
  ESEM 2008.

22
Constructing Predictors

• Baseline Naive Bayes.
• Why? Best reported results so far (Menzies et
  al., 2007)
• Remove assumptions and construct different
  models.
• Independent Attributes -gtMultivariate dist.
• Attributes of equal importance

• "Software Defect Prediction Heuristics for
  Weighted Naïve Bayes", B. Turhan and A. Bener,
  ICSOFT2007, Barcelona, Spain, July 2007.
• Software Defect Prediction Modeling, B. Turhan,
  IDOESE 2007, Madrid, Spain, September 2007
• Yazilim Hata Kestirimi için Kaynak Kod
  Ölçütlerine Dayali Bayes Siniflandirmasi,
  UYMS2007, Ankara, September 2007
• A Multivariate Analysis of Static Code
  Attributes for Defect Prediction, B. Turhan and
  A. Bener QSIC 2007, Portland, USA, October 2007.
• Weighted Static Code Attributes for Defect
  Prediction, B.Turhan and A. Bener, IEEE Trans.on
  Software Eng. (under review)

23
WC vs CC Data?

• When to use WC or CC?
• How much data do we need to construct a model?

Implications of Ceiling Effects in Defect
Predictors, Menzies, T., Turhan, B., Bener, A.,
Gay, G., Cukic, B., Jiang, Y. PROMISE 2008,
Leipzig, Germany, May 2008. Cross- vs
Within-Company Defect Prediction Studies,
Menzies, T., B. Turhan, A. Bener, and J.
Distefano, 2008, TSE- revised and resubmitted.
24
Module Structure vs Defect Rate

• Fan-in, fan-out
• Page Rank Algorithm
• Call graph information on the code
• small is beautiful

Software Defect Prediction Using Call Graph
Based Ranking Algorithm, Koçak, G., Turhan, B.,
Bener, A. Euromicro 2008.
25
Cost Estimation

• Comparison of ML based models with parametric
  models
• Feature ranking
• COCOMO81- COCOMO2-COQUALMO
• Cost estimation as a classification problem
  (interval prediction)

• "Mining Software Data", B. Turhan and O.
  Kutlubay, Data Mining and Business Intelligence
  Workshop in ICDE'07 , Istanbul, April 2007
• Software Effort Estimation Using Machine
  Learning Methods, B. Baskeles, B.Turhan, A.
  Bener, ISCIS 2007,Ankara, November 2007.
• Feature Weight Assignment in Analogy-Based Cost
  Estimation Tosun, A., Turhan, B. And Bener, A.,
  2007, under review in Software Quality Journal.
• "Evaluation of Feature Extraction Methods on
  Software Cost Estimation", B. Turhan, O.
  Kutlubay, A. Bener, ESEM2007, Madrid, Spain,
  September 2007 .
• A New Perspective on Data Homogeneity in Cost
  Estimation A Study in Embedded Systems Domain
  (2008). Bakir A., Turhan, B. Bener, A., Journal
  of Systems and Software, under review.ENNA
  Software Effort Estimation Using Ensemble of
  Neural Networks with Associative Memory Kültür
  Y., Turhan B., Bener A., FSE 2008.
• Software Cost Estimation as a Classification
  Problem, Bakir, A., Turhan, B., Bener, A. ICSOFT
  2008.

26
Prest

• A tool developed by Softlab
• Parser
• C, Java, C
• Metric Collection
• Data Analysis

27
Data Sources

• Public Datasets
• NASA (IVV Facility, Metrics Program)
• PROMISE (Software Engineering Repository)
• Includes Softlab data now
• Open Source Projects (Sourceforge, Linux, etc.)
• Internet based small datasets
• Softlab Data Repository (SDR)
• Local industry collaboration
• Total 20 companies, 25 projects over 5 years

28
Process Automation

• UML Refactoring
• Class diagram source code
• Tool
• Algorithm (graph based)
• What needs to be refactored
• Complexity vs call graphs

Y. Kösker and A. Bener . "Synchronization of UML
Based Refactoring with Graph Transformation",
SEKE 2007, Boston, July 9-11, 2007
29
Process Improvement and Assessment

• A Case in health care industry
• Process Improvement with CMMI
• Requirements Management
• Change Management
• Comparison A Before and After Evaluation
• Lessons Learned

The Benefits of Quality (CMMI) Project in a SME
A Before and After Comparison, (2008) Tosun, A.,
Turhan, B., Bener, A., submitted to EMSE
30
IT Audit/ Assessment

• Certified chief auditor by Turkish Financial
  Services Authority (BDDK)
• Audited 3 banks and 2 insurance companies
• Training and consultancy in
• COBIT, SOX, ITIL, ISO 27000-27001

31
SoftLab and Industry Collaboration

• Training
• Seminars and short courses on fundamentals of
  software engineering
• Software Engineering Methodologies
• Processes (Requirements, Design, Coding, Testing
  and Maintenance, etc.)
• Software Quality and Software Quality Management
• Joint Projects
• Project Management
• Reuirements Analysis and engineering
• Processes and Process Improvement
• Development of metric programs and establishing
  metric data sets
• Financial applications
• Telecom
• White goods embedded systems
• Health care
• Automotive

32
SOA and Web Services

• Web Services discovery and composition
• Mobile Web Services
• Semantic Web Services
• Semantic Matching Algorithms

33
Mobile Web Services

• To reach desktop applications ubiquously
• Multiple platforms
• web services could be the solution
• Transaction time and network load analysis of web
  services on mobile networks

M. Adaçal ve A. Bener, Mobile Web Services A
New Agent Based Framework, IEEE Internet
Computing Journal, May-June 2006, vol.3, pp.
58-65.
34
Semantic Web Services

• Service discovery based on graph based algorithm
• A framework for semantic discovery of web
  services
• Semantic similarity and distance description and
  matching in ontologies

Ozadali, V., Bener, A., E.S. Ilhan, (2008),
"SAM Semantic Advanced Matchmaker with
Precondition and Effect Matching Using SWRL"
submitted to IEEE Intelligent Systems. S.
Ozyilmaz, G.B. Akkus and A. Bener,Matchmaking in
semantically enhanced web services inductive
ranking methodology, ICSSEA 2007, Paris,
December 4-6, 2007 E.S. Ilhan, and A. Bener,
Improved Service Ranking and Scoring Semantic
Advanced Matchmaker (SAM) Architecture, ENASE
2007, Barcelona, July22-25, 2007 E.S. Ilhan, G.B.
Akkus and A. Bener, SAM Semantic Advanced
Matchmaker, SEKE 2007, Boston, July 9-11,
2007. E.Ayorak, and A. Bener, Superpeer Web
Service Discovery Architecture, ICDE 2007,
Istanbul, April 15-20, 2007. M. Sensoy, F.C.
Pembe, H. Zirtiloglu, P.Yolum and A.Bener,
Experience-based Service Provider Selection in
Agent Mediated E-Commerce, the International
Journal of Engineering Applications of AI,
April-May, 2007. Senvar, M. and Bener, A., 2006,
Matchmaking of Semantic Web Services Using
Semantic-Distance Information, Lecture Notes in
Computer Science by Springer Verlag, ADVIS 2006,
October 18-20, Izmir, Turkey.
35
Contact Details

• Ayse Bener bener_at_boun.edu.tr
• Burak Turhan turhanb_at_boun.edu.tr
• For more information
• http//softlab.boun.edu.tr