Research Challenges

1. SoftLabBoğaziçi University Department of Computer EngineeringSoftware Engineering Research Labhttp://softlab.boun.edu.tr/

2. Research Challenges Trend to large, heterogenous, distributed sw systems leads to an increase in system complexity Software and service productivity lags behind requirements Increased complexity takes sw developers further from stakeholders Importance of interoperability, standardisation and reuse of software increasing.

3. Research Challenges Service Engineering Complex Software Systems Open Source Software Software Engineering Research

4. Software Engineering Research Approaches Balancing theory and praxis How engineering research differs from scientific research The role of empirical studies Models for SE research

5. The need to link research with practice Why after 25 years of SE has SE research failed to influence industrial practice and the quality of resulting software? Potts argues that this failure is caused by treating research and its application by industry as separate, sequential activities. What he calls the research-then-transfer approach. The solution he proposes is the industry-as-laboratory approach. . Colin Potts, Software Engineering Research Revisited, IEEE Software, September 1993

6. Research-then-Transfer Research Solution V1 Problem V1 Wide gulf bridged by indirect, anecdotal knowledge Research Solution V2 Problem V2 Problem V3 Research Solution V3 Problem V4 Research Solution V4 Technology transfer Gap bridged by hard, but frequently inappropriate technology Problem evolves invisibly to the research community IncrementalRefinement of research solutions

7. Research-then-Transfer Problems Both research and practice evolve separately Match between current problems in industry and research solutions is haphazard No winners

8. Disadvantages of Research-then-Transfer Research problems described and understood in terms of solution technology - whatever is current research fashion. Connection to practice is tenuous. Concentration is on technical refinement of research solution - OK but lacks industrial need as focus, so effort may be misplaced. Evaluation is difficult as research solutions may use technology that is not commonly used in industry Delay in evaluation means problem researchers are solving has often evolved through changes in business practice, technology etc. Transfer is difficult because industry has little basis for confidence in proposed research solution.

9. Industry-as-Laboratory Approach to SE research Problem V1 Research Solution V1 Problem V2 Research Solution V2 Problem V3 Research Solution V3 Problem V4 Research Solution V4

10. Advantages of Industry-as-Laboratory Approach Stronger connection at start because knowledge of problem is acquired from the real practitioners in industry, often industrial partners in a research consortium. Connection is strengthened by practitioners and researchers constantly interacting to develop the solution Early evaluation and usage by industry lessens the Technology Transfer Gap. Reliance on Empirical Research shift from solution-driven SE to problem-focused SE solve problems that really do matter to practitioners

11. Early SEI industrial survey research What a SEI survey* learned from industry: There was a thin spread of domain knowledge in most projects Customer requirements were extremely volatile. These findings point towards research combining work on requirements engineering with reuse - instead of the approach of researching these topics by separate SE research communities - as is still found today! *From ‘A field study of the Software Development Process for Large Systems’, CACM, November 1988.

12. Further Results from Potts et al Early 90s Survey 23 software development organizations (during 1990-92). (Survey focused on Requirements Modeling process) Requirements were invented not elicited. Most development is maintenance. Most specification is incremental. Domain knowledge is important. There is a gulf between the developer and user User-interface requirements continually change. There is a preference for office-automation tools over CASE tools to support development. I.e. developers found using a WP + DB more useful than any CASE tools.

13. Industry-as-Laboratory emphasizes Real Case Studies Advantages of case studies over studying problems in research lab. Scale and complexity - small, simple (even simplistic) cases avoided - these often bear little relation to real problems. Unpredictability - assumptions thrown out as researchers learn more about real problems Dynamism - a ‘real’ case study is more vital than a textbook account The real-world complications of industrial case studies are more likely to throw up representative problems and phenomena than research laboratory examples influenced by the researchers’ preconceptions.

14. Need to consider Human/Social Context in SE research Not all solutions in software engineering are solely technical. There is a need to examine organizational, social and cognitive factors systematically as well. Many problems are “people problems”, and require “people-orientated” solutions.

15. Theoretical SE research While there is still a place for innovative, purely speculative research in Software Engineering, research which studies real problems in partnership with industry needs to be given a higher profile. These various forms of research ideally complement one another. Neither is particularly successful if it ignores the other. Too industrially focused research may lack adequate theory! Academically focused research may miss the practice!

16. Research models for SE Problem highlighted by Glass*: Most SE Research in 1990s was Advocacy Research. Better research models needed. The software crisis provided the platform on which most 90s research was founded. SE Research ignored practice, for the most part; lack of practical application and evaluation were gapping holes in most SE research. Appropriate research models for SE are needed. * Robert Glass, The Software -Research Crisis, IEEE Software, November 1994

17. Methods underlying Models Scientific method Engineering method Empirical method Analytical method From W.R.Adrion, Research Methodology in Software Engineering, ACM SE Notes, Jan. 1993

18. Scientific method Observe real world Propose a model or theory of some real world phenomena Measure and analyze above Validate hypotheses of the model or theory If possible, repeat

19. Engineering method Observe existing solutions Propose better solutions Build or develop better solution Measure, analyze, and evaluate Repeat until no further improvements are possible

20. Empirical method Propose a model Develop statistical or other basis for the model Apply to case studies Measure and analyze Validate and then repeat

21. Analytical method Propose a formal theory or set of axioms Develop a theory Derive results If possible, compare with empirical observations Refine theory if necessary

22. Need to move away from purely analytical method The analytical method was the most widely used in mid-90s SE research, but the others need to be considered and may be more appropriate in some SE research. Good research practice combines elements on all these approaches.

23. 4 important phases for any SE research project (Glass) Informational phase - Gather or aggregate information via reflection literature survey people/organization survey case studies Propositional phase - Propose and build hypothesis, method or algorithm, model, theory or solution Analytical phase - Analyze and explore proposal leading to demonstration and/or formulation of principle or theory Evaluation phase - Evaluate proposal or analytic findings by means of experimentation (controlled) or observation (uncontrolled, such as case study or protocol analysis) leading to a substantiated model, principle, or theory.

24. Software Engineering Research Approaches The Industry-as-Laboratory approach links theory and praxis Engineering research aims to improve existing processes and/or products Empirical studies are needed to validate Software Engineering research Models for SE research need to shift from the analytic to empirical.

25. Empirical SE Research

26. SE Research Intersection of AI and Software Engineering An opportunity to: Use some of the most interesting computational techniques to solve some of the most important and rewarding questions

27. AI Fields, Methods and Techniques

28. What Can We Learn From Each Other?

29. Software Development Reference Model Intersection of AI and SE Research Empirical Software Engineering

30. Intersection of AI and SE Research Build Oracles to predict Defects Cost and effort Refactoring Measure Static code attributes Complexity and call graph structure Data collection Open repositories (NASA, Promise) Open source Softlab Data Repository (SDR)

31. Software Engineering Domain Classical ML applications Data miner performance The more data the better the performance Little or no meaning behind the numbers, no interesting stories to tell

32. Software Engineering Domain Algorithm performance Understanding Data Change training data: over/ under/ micro sampling Noise analysis Increase information content of data Feature analysis/ weighting Learn what you will predict later Cross company vs within company data Domain Knowledge SE ML

33. In Practise Product quality Lower defect rates Less costly testing times Low maintenance cost Process quality Effort and cost estimation Process improvement

34. Software Engineering Research • Predictive Models • Defect prediction and cost estimation • Bioinformatics • Process Models • Quality Standards • Measurement

35. Software Measurement Defect Prediction/ Estimation Effort & Cost Estimation Process Improvement (CMM) Major Research Areas

36. Defect Prediction • 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

37. A Testing Workbench

38. c > 0 c Static Code Attributes • void main() • { • //This is a sample code • //Declare variables • int a, b, c; • // Initialize variables • a=2; • b=5; • //Find the sum and display c if greater than zero • c=sum(a,b); • if c < 0 • printf(“%d\n”, a); • return; • } • int sum(int a, int b) • { • // Returns the sum of two numbers • return a+b; • } LOC: Line of Code LOCC: Line of commented Code V: Number of unique operands&operators CC: Cyclometric Complexity

39. Defect Prediction • Machine Learning based models. • Defect density estimation • 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. B.Turhan, A. Tosun and A. Bener, "An Industrial Application of Classifier Ensembles for Locating Software Defects". Submitted to Information and Software Technology Journal, 2008.

40. 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 -> Weighted Naive Bayes "Software Defect Prediction: Heuristics for WeightedNaïve 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, SEKE 2008, San Francisco, July 2008. B.Turhan and A. Bener, "Analysis of Naive Bayes' Assumptions on Software Fault Data: An Empirical Study". Data and Knowledge Engineering Journal, 2008, in print B.Turhan, A. Tosun and A. Bener, "An Industrial Application of Classifier Ensembles for Locating Software Defects". Submitted to Data and Knowledge Engineering Journal, 2008. B.Turhan, A. Bener and G. Kocak "Data Mining Source Code for Locating Software Bugs: A Case Study in Telecommunication Industry". Submitted to Expert Systems with Applications Journal, 2008.

41. WC vs CC Data for Defects? • 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. “Nearest Neighbor Sampling or Cross Company Defect Predictors”, Turhan, B., Bener, A., Menzies, T., DEFECTS 2008, Seattle, USA, July 2008. "On the Relative Value of Cross-company and Within-Company Data for Defect Prediction", B. Turhan, T. Menzies, A. Bener, J. Distefano, Empirical Software Engineering Journal, 2008, in print T. Menzies, Z.Milton, B. Turhan, Y. Jiang, G. Gay, B. Cukic, A. Bener, "Overcoming Ceiling Effects in Defect Prediction", Submitted to IEEE Transactions on Software Engineering, 2008.

42. Module Structure vs Defect Rate • Fan-in, fan-out • Page Rank Algorithm • Dependency graph information • “small is beautiful” Koçak, G., Turhan, B., Bener,A. “Software Defect Prediction Using Call Graph Based Ranking Algorithm”, Euromicro 2008. G. Kocak, B. Turhan and A.Bener, "Predicting Defects in a Large Telecommunication System”, ICSOFT'08.

43. COST ESTIMATION • Cost Estimation: predicting the effort required todevelop a new software project • Effort: the number of months one person would need to develop a given project (personmonths-PM) • CE assists project managers when they make important decisions (bidding,planning, resourceallocation): • underestimation  approve projects that would then exceed their budgets • overestimation  waste of resources • Modeling accurate & robust cost estimators = Successful software project management

44. COST ESTIMATION • Understanding the data structure? • CROSS- vs. WITHIN-APPLICATION DOMAIN embedded software domain • Better predictor? • Point Estimation: a single value of effort is tried to be estimated • Interval Estimation:effort intervals are tried to be estimated COST CLASSIFICATION dynamic intervals classification algorithms point estimates

45. COST ESTIMATION • How can we achieve accurate estimations with limited amount of effort data? • feature subset selection: Save the cost of extracting less important features

46. 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 LearningMethods”, B. Baskeles, B.Turhan, A. Bener, ISCIS 2007,Ankara, November 2007. "Evaluation of Feature Extraction Methods on Software Cost Estimation",B. Turhan, O. Kutlubay, A. Bener,ESEM2007, Madrid, Spain, September 2007. “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. B.Turhan, A. Bakir and A. Bener, "A Comparative Study for Estimating Software Development Effort Intervals". Submitted to Knowledge Based Systems Journal, 2008. B.Turhan, Y. Kultur and A. Bener, "Ensemble of Neural Networks with Associative Memory (ENNA) for Estimating Software Development Costs", Submitted to Knowledge Based Systems Journal, 2008. A. Tosun, B. Turhan, A. Bener, "Feature Weighting Heuristics for Analogy Based Effort Estimation Models", Submitted to Expert Systems with Applications, 2007. A. Bakir, B.Turhan and A. Bener, "A New Perspective on Data Homogeneity for Software Cost Estimation". Submitted to Software Quality Journal, 2008.

47. Prest • A tool developed by Softlab • Parser • C, Java, C++, jsp • Metric Collection • Data Analysis

48. Data Sources • 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 • University of South California (USC) Dataset • Desharnais Dataset • ICBSG Dataset • NASA COCOMO and NASA 93 Datasets • Softlab Data Repository (SDR) • Local industry collaboration • Total 20 companies, 25 projects over 5 years

49. 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 B.Turhan, Y. Kosker and A. Bener, "An Expert System for Determining Candidate Software Classes for Refactoring". Submitted to Expert Systems with Applications Journal, 2008. Y. Kosker, A.Bener and B. Turhan, "Refactoring Prediction Using Class Complexity Metrics”, ICSOFT'08, 2008. B. Turhan, A. Bener and Y.Kosker, "Tekrar Tasarim Gerektiren Siniflarin Karmasiklik Olcutleri Kullanilarak Modellenmesi" (in Turkish), 2. Ulusal Yazilim Mimarisi Konferansi (UYMK'08), 2008.

50. 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 • Tosun, B. Turhan and A. Bener,"The Benefits of a Software Quality Improvement Project in a Medical Software Company: • A Before and After Comparison", Invited Paper and Keynote speech in International Symposium on Health Informatics and • Bioinformatics (HIBIT'08), 2008.