Software Engineering Research & Development at A.U.Th

1. Software Engineering Research & Developmentat A.U.Th Ioannis Stamelos Panagiotis Katsaros {stamelos, katsaros}@csd.auth.gr

2. Presentation Outline • Introduction • AUTH / Dept of Informatics • Software Engineering Research Group • Software Quality, Management, Education • Open Source Software Engineering / Extreme Programming Sweden

3. Introduction Sweden

4. Sweden

5. City of Thessaloniki • Capital of Macedonia region, northern Greece • ~1 M inhabitants, 2nd greek city, major port of Balkans • 2300 years of continuous history, joined modern Greece in 1912 Sweden

6. Sweden

7. AUTH / Dept of Informatics Sweden

8. Aristotle University of Thessaloniki, Greece • First school (Philosophy) established in 1925 • Largest Greek University • 45 departments • > 60.000 students • > 3.000 staff / teachers • www.auth.gr Sweden

9. Department of Informatics • School of Physical Science, plans to move to Polytechnic School • Established in 1993 • 22 academic staff members • ~700 students • www.csd.auth.gr Sweden

10. 4-years Degree Curriculum • 8 semesters • 5 semesters with obligatory courses • 3 semesters with options out of 4 directions: • Information Systems • Digital Media • Multimedia / Educational Technologies • Networks / Architecture / Communications • Thesis Project Sweden

11. Master Courses • MSc in Informatics (4 directions) • MSc in Informatics & Management (with Dept. of Business Management) Plus • MSc in Medical Informatics Sweden

12. Five Research Labs • Programming Languages and Software Engineering – PLASE • Data Technologies and Engineering -DELAB • Information Processing and Artificial Intelligence • Multimedia (Educational Technologies) • Networks, Communications and Architecture Sweden

13. Programming Languages and Software Engineering – PLASE • 6 staff members • 10 research associates • 20 PhD students • Major research areas: • Intelligent Systems • Web Engineering • Software Engineering • Mathematics Sweden

14. Software Engineering Group (SWENG) • 3 ac. staff members: • Assistant Professors I. Stamelos, L. Angelis / PLASE • P. Katsaros / DELAB • 5 research associates • 10 PhD students (joined the group during last 4 years) • http://sweng.csd.auth.gr Sweden

15. SWENG: Teaching • Dept. of Informatics / Hellenic Open University (ODL) • Mathematics, Graph Theory • Language Theory, Compilers • Introduction to Software Engineering • Object Oriented Analysis / Java / C++ • Master Courses • Software Project Management • Enterprise Information Systems Sweden

16. SWENG: R&D Activities • Basic Research • Collaboration with other research teams in Greece and in EU: • Politecnico di Torino (Prof. Morisio) • Univ. LAMSADE / Paris (Prof. Tsoukias) • Univ. Juan Carlos / Madrid (Prof. Barahona) • Funded Applied Research & Development • Greek Public Sector • Greek Secretariat for Research and Technology • Private Companies • EU Sweden

17. SWENG: Funding • Software industry context: • Public Sector still developing IT infrastructure (e.g. transportation) • Private Sector companies enhancing IT infrastructure (banks, utilities, privatized public companies) • Most large SW Companies mainly acting as vendors (e.g. of ERP systems) • Various small but aggressive SW companies acting as developers • Funding: limited but growing fast • (in last 5 years, while SWENG group was growing) ~0,7 M euros • mostly from development projects • participated also to other PLASE projects (MATHIND, Go Net, …) • Now shifting to research projects as well: • obtained ~0,5 M, expecting another 0,6 M in years 2006 - 2007 (mainly from EU, GSRT) • see forthcoming research projects at Forthcoming Research Projects slide Sweden

18. Examples of current / completed projects • Expert System for Software Evaluation • Automated Certification System (with data mining functionality) • Enterprise Knowledge Management System • Specification of various public company information systems (e.g. ticketing) Sweden

19. SWENG: Major Research Areas • Software Quality • Dependability, Security • Software Cost Estimation • Software Evaluation • Open Source Software • Extreme Programming • Software Education Sweden

20. Software Quality, Management, Education Sweden

21. Software Quality (1) • Problems: • how to quantify and predict / assess SW quality attributes • many SW quality metrics /methods have been proposed but have not been sufficiently validated • Need for • novel, intuitively confirmed measures • empirical evidence for assessing methods, tools, approaches Sweden

22. Software Quality (2) • Formal experiments • Most powerful empirical investigation tool, producing generic conclusions • A typical formal experiment setting: • Two groups of subjects are given the same object, differing only in the controlled variable (e.g. design of the same system based on two different design approaches, see next two slides) • Subjects are asked to perform the same task • All other potentially affecting variables are kept constant (e.g. group experience) • The two groups’ performance is monitored accurately and statistical tests are run to detect statistical difference Sweden

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25. Software Quality (3) • Currently investigating quality assessment based on Heuristics - A Heuristic is a good practice (e.g. avoidance of ‘God’ classes in OO design, see previous slide) • Developed tools assessing compliance with Riel’s set of object-oriented heuristics (based on C++/Java code, UML class diagrams) • Design of new methods / models for quality prediction • Software Defect Prediction Using Regression via Classification(AICSSA ’06) Sweden

26. Decision tree for defect prediction (for a specific data set) Sweden

27. Software Cost Estimation • The problem: predict the human effort for developing a system • Expert judgment • Analogy based method, project is compared with past projects, project cost is proportional to cost of ‘similar’ projects • Algorithmic / ad hoc models, a set of equations are used (e.g. the COCOMO models) • Issues: • Wrong, inaccurate, missing past projects data • Uncertainty about new project • Many categorical values (e.g. type of system, language used) • Method accuracy • Calibration with local data • Method suitability to local situation (e.g. local project data may be not available or projects are not measured accordingly) Sweden

28. Software Cost Estimation (1) • Interval estimation • A range of values is produced • More realistic approach w.r.t to a point estimate • Interval incorporates model error • Interval accounts for estimation data inaccuracy • May be easily transformed to a point estimate (e.g. take the mean value of the interval) • A number of techniques has been proposed Sweden

29. Software Cost Estimation (2) • Improvement of Analogy Based Estimation (Bootstrap sampling method) • To calibrate method parameters (e.g. number of similar projects used for the estimation) • To produce interval estimates • Project portfolio estimation • Combination of interval estimates from portfolio project members • Categorical, multinomial, ordinal regression • Regression models that provide interval estimates • Intervals are often predefined by managers • Produced more accurate results than previously proposed methods (e.g. OLS) Sweden

30. BRACE: a tool for advanced estimation by analogy Screenshot of BRACE showing the results of a calibration session Sweden

31. BRACE: interval estimate generation New project is estimated to demand an effort of 2000-2500 mh (prob. 65,1%), 2500-3000 (prob. 23,1%), etc. Sweden

32. Software Cost Estimation (3) • How to deal with missing data in your historical data base? • Imputation methods: replace missing values • Proposed Multiple Logistic Regression as an imputation method for missing categorical values • MLR found to perform better w.r.t. to previously proposed methods Sweden

33. Software Cost Estimation (4) • Comparative assessment of proposed methods (focusing on AI methods) • Association Rules, BBN, CART • AR are found to be the most accurate / promising approach Sweden

34. Example Association Rule for Productivity Estimation Association Rule Support Confidence Rule Body Rule Head 6.3 66.6 [ACAP_H]+[DATA_N] ==> [PROD_4] Interpreted as follows: When the programmers’ analysis capability is high and the database size is nominal then the productivity is likely to be in the fourth category (100<PROD<160 KLOC/MM) Sweden

35. Software process modeling with iterative Bayesian Belief Networks Sweden

36. Software Evaluation • Application of MCDA (Multiple Criteria Decision Aid) on software problem situations Sweden

37. Profile evaluation of software artifacts / processes User defines profiles as sets of attribute values, then product / process is assessed through the Electre method to decide its profile value Sweden

38. Software Engineering Education • Investigation of novel approaches in SE education through formal experiments • Distance learning of UML (sequence diagrams) • Use of a lesson sheet for teaching Usability Sweden

39. Open Source Software Engineering / Extreme Programming Sweden

40. Free / Open Source Software (1) • Revolutionary software development • Issues: • What is the quality of F/OSS products? • How can we assess an F/OSS project? • Is it active? • Is it interesting? • How can we predict an F/OSS project? • How will it grow in terms of code size, number of programmers? Sweden

41. Free / Open Source Software (2) • F/OSS product quality assessment • F/OSS process modeling • Dynamic models for predicting F/OSS project behavior • Knowledge sharing and learning in F/OSS Sweden

42. F/OSS product quality assessment • Case Studies • Measured and assessed 100 LINUX applications: applications’ code found to be of at least equal quality w.r.t. to closed source code • Measured and assessed specific software systems’ quality (e.g. assessed COMPIERE code, an Open Source ERP system, found to be of excellent quality) • Quality assessment horizontal studies • Measured, assessed 5 F/OSS projects and compared with closed source counterparts using Maintainability Index (SEI) • F/OSS systems found to exhibit superior maintainability, however F/OSS maintainability deteriorated in similar way as in closed source Sweden

43. F/OSS maintainability evolution Sweden

44. F/OSS process modeling • Dynamical F/OSS process model • Based on programmer motivation • Elaborates on programmer tasks (coding, debugging), modules, defects • Calibrated on typical F/OSS projects (e.g. Apache, Gnome) • Predicting curves for size (LOCs), number of programmers Sweden

45. Knowledge sharing and learning in F/OSS • Modeling of knowledge sharing in F/OSS • Knowledge sharing model • Mining F/OSS mail lists to determine knowledge brokers • Learning mechanisms in F/OSS • Learning in F/OSS occurs following the constructivist model • Ultimate goal: use of F/OSS projects in SE teaching • Ran a pilot study: 15 students practiced software testing by participating in F/OSS projects, obtained promising results Sweden

46. F/OSS Knowledge Sharing Model Knowledge is generated, shared and acquired by traversing various paths of the graph model in many different ways Sweden

47. F/OSS Mailing List Social Network Sweden

48. Extreme Programming • Surveying Agile Methods / XP spread in Greece • SW SMEs use XP methods without knowing it! • Investigation of human factors • Developer personality and temperament when forming a pair in pair programming • Conducted formal experiments with students • Found that mixed personalities and temperaments produce better results Sweden

49. Forthcoming Funded Research Projects • SQO – OSS (IST/EU), Software quality observatory for Open Source, a system that will continuously monitor and evaluate OSS products • FLOSSMETRICS (IST/EU), studies and models for OSS process management • F/OSS in education (MINERVA / EU), exploitation of OSS in informatics education • DIERGASIA (means PROCESS in Greek) (TELETEL / GSRT), prediction models and procedures for advanced software quality assurance in a TLC systems company Also bidding for funding for various development projects (training in basic computer skills using ODL, portal development for technology museums, …) Sweden

50. Current / Future Research topics • F/OSS quality assessment through the use of heuristics • Social Networks in F/OSS • Indicators for community decay in F/OSS projects: when is the project endangered because of mentors / key developers abandonment? • Software Architecture • Exploit architectural tactics in component specification to support reusability (ICSR’06, Torino) • Management Anti-Patterns • Modeling with BBN • XP management anti-patterns • Intellectual Software Capital Valuation • How much is a software artifact (component, method, piece of knowledge) worth? • Investigate XP through experiments with professionals • Introduction of F/OSS projects in SE education Sweden