1. SoftLab Boğaziçi University Department of Computer Engineering Software Engineering Research Lab http://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. 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

7. 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.

8. 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.

9. 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!

10. 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.

11. Empirical SE Research

13. Real life Problems

14. Do parts connect?

16. Research Questions:

21. Our Research Question 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

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

23. Research Question How much test is enough? When to stop testing?

24. Problem Decision making under uncertainity

25. Solution CS claims it can be solved Artificial Intelligence

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

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

35. A Testing Workbench

36. 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; } ModuleLOCLOCCVCCError main()164522 sum()51310 LOC: Line of Code LOCC: Line of commented Code V: Number of unique operands&operators CC: Cyclometric Complexity c > 0 c

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

38. 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 Data Sources

39. Tangible Benefit ProjectsGerçek hatalı dosya sayısı pdpfİncelenen modül sayısı Test eforunda kazanım XXX5%63%931%84 YYY1%100%1027%87 ZZZ1%100%23103%77

40. Requirements Analysis Coding Design Test Maintenance  ∞ Defect prediction Call Graph / Refactoring Refactoring Matching reqs with defects Test driven development

41. Emerging Research Topics Adding organizational factors to local prediction model Information about the development team, experience, coding practices, etc. Adding file metrics from version history Modified/added/deleted lines of code Selecting only modified files from each version in the prediction model Confidence Factor Using time factors Dynamic prediction: Constructing a model for each application in a version for each module/package in an application for each developer by learning from his/her coding habits TDD Measuring test coverage Defect proneness Company wide implementation process Embedded systems Cost/ Effort Estimation Dynamic estimation per process