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SoftLab Boğaziçi University Department of Computer Engineering

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  • 1. SoftLab Boğaziçi University Department of Computer Engineering Software Engineering Research Lab http://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 (~5 0 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 : 21 + 8 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
    • TÜBİTAK, 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
    Bo ğaziçi Üniversitesi Bilgisayar Mühendisliği Bölümü
  • 8. AILAB
    • Artificial Intelligence Research Lab
      • 2005 world champion, 2006 Robocup first 8!
      • Scored a goal to M icrosoft 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
    • SOS LAB
      • Complex Systems
  • 13.
    • Heterogenous and distributed systems
    • Complex systems
    • Standards
    • Integration
    • Resuse
    Software Enginering Challenges 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.
    • 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?
    Code Metrics
  • 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 > 0
    • 2 things needed:
      • Raw data: Source code
      • Software Metrics -> 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, D ata Mining and Business Intelligence Workshop in ICDE'07 , İstanbul, 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 ->Multivariate dist.
      • Attributes of equal importance
    " S oftware D efect P rediction : Heuristics for Weighted N aïve B ayes ", B. Turhan and A. Bener , ICSOFT2007, Barcelona, Spain, July 2007. “ Software Defect Prediction Modeling”, B. Turhan, IDOESE 2007, Madrid, Spain, September 2007 “ Yazılım Hata Kestirimi için Kaynak Kod Ölçütlerine Dayalı Bayes Sınıflandırması ”, 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, D ata Mining and Business Intelligence Workshop in ICDE'07 , İstanbul, 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, ESEM 2007 , 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”, Bakır, A., Turhan, B., Bener, A. ICSOFT 2008.
  • 26. Prest
    • A tool developed by Softlab
    • Parser
      • C, Java, C++
    • Metric Collection
    • Data Analysis
  • 27.
    • Public Datasets
      • NASA (IV&V 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
    Data Sources
  • 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.
    • 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
    SoftLab and Industry Collaboration
  • 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. Akkuş and A. Bener,”Matchmaking in semantically enhanced web services: inductive ranking methodology”, ICSSEA 2007, Paris, December 4-6, 2007 E.S. İlhan, and A. Bener, “Improved Service Ranking and Scoring: Semantic Advanced Matchmaker (SAM) Architecture”, ENASE 2007, Barcelona, July22-25, 2007 E.S. İlhan, G.B. Akkuş 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. Şensoy, F.C. Pembe, H. Zırtıloğlu, 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. Şenvar, 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, İzmir, Turkey.
  • 35.
      • Ayşe Bener: [email_address]
      • Burak Turhan: [email_address]
    • For more information:
      • http://softlab.boun.edu.tr
    Contact Details