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: bener@boun.edu.trBurak Turhan: turhanb@boun.edu.tr

For more information:http://softlab.boun.edu.tr

Contact Details