softlab boğaziçi university department of computer engineering
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SoftLabBoğaziçi University Department of Computer Engineering
Software Engineering Research Labhttp://softlab.boun.edu.tr/
Contents Department of Computer
Engineering Undergrad education Research Labs
SoftLab Background Research Areas and Sample Work Industry Funded Projects
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
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.
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.
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
Strong Research Labs 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
Boğaziçi Üniversitesi Bilgisayar Mühendisliği Bölümü
AILAB Artificial Intelligence Research Lab
2005 world champion, 2006 Robocup first 8!
Scored a goal to Microsoft team...
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
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
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
Other Research Labs MEDIALAB
Multimedia EDALAB
Embedded Systems
CASLAB Computer Systems
Architecture SOSLAB
Complex Systems
Heterogenous and distributed systems
Complex systems Standards Integration Resuse
Software Enginering Challenges
SOA and Web Services Software Quality - processes
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
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
Problem 1
How to tell if the project is on schedule and within budget?
Earned-value charts.
Problem 2
How hard will it be for another organization to maintain this software?
McCabe Complexity
Problem 3
How to tell when the subsystems are ready to be integrated
Defect Density Metrics.
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
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
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 , 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.
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
"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
“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)
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.
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.
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 , 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, 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”, Bakır, A., Turhan, B., Bener, A. ICSOFT 2008.
Prest
A tool developed by Softlab
Parser C, Java, C++
Metric Collection Data Analysis
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
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
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
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
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
SOA and Web Services Web Services discovery and
composition Mobile Web Services Semantic Web Services Semantic Matching Algorithms
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.
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, 2007E.S. İlhan, and A. Bener, “Improved Service Ranking and Scoring: Semantic Advanced Matchmaker (SAM) Architecture”, ENASE 2007, Barcelona, July22-25, 2007E.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.
Ayşe Bener: [email protected] Turhan: [email protected]
For more information:http://softlab.boun.edu.tr
Contact Details