Latest trends in Global Software Industry

1. Latest trends in Global Software Industry Dr. Jeyakesavan Veerasamy jeyak7@gmail.com jeyv@utdallas.edu

2. Agenda • Introduce myself • Software: Then & Now • SW development environment options • SW Efficiency – does it matter? • Latest buzz • Trends in CS education • Open Q&A

3. Dr. V. Jeyakesavan: Academia, Industry & Personal • Dad was a school teacher • B.E. (ECE) in CEG Guindy, Anna University – 1986-90 • UNIX System Software Engineer, HCL Limited, Chennai, 1990-91 • MS Computer Science, University of Texas at Dallas (UTD), 1991-94

4. Dr. V. Jeyakesavan: Academia, Industry & Personal … • Telecom Software Engineer, Northern Telecom, Dallas, 1994-97 • Ph.D. Computer Science (part-time), University of Texas at Dallas (UTD), 1994-99 • Technical Lead, Samsung Telecom, 1997-2010 • Got married in 1998 • Adjunct Faculty, UTD CS department, 1999-2002 • Online Adjunct Faculty in several online universities from 2000

5. Dr. V. Jeyakesavan: Academia, Industry & Personal … • Adjunct Faculty, Southern Methodist University, 2010 • Sr. Lecturer (full-time), UTD Computer Science, 2010-present • 2 daughters: Nila (8) and Chinmayee (4) • Passionate about teaching – happy to share ideas to improve teaching quality in colleges

6. Dr. V. Jeyakesavan: Summary • 18 years experience as Software Engineer • 12 years of teaching experience

7. Fun way to learn programming & OOP • Alice, a visual programming language • Download from www.alice.org • Whole world can be characterized by OOP • How many meaningful objects in this room? • What is a class? • Alice program demo • Great stepping stone to serious programming • Share it with your friends & family

8. Software: Then & Now Note: Almost all the content is based on observations & experience only.

9. Complexity of software projects: historical snippets • Ad-hoc development model • Waterfall model • slow & unpredictable • heavy documentation effort • cost overruns & missed deadlines

10. My experience with huge SW projects(Waterfall model) • Period of instability • collisons during merge • product delays • Is it good enough to release?

11. Agile process

12. Agile methods • Limited documentation • Better tracking of current status • Frequent releases • Continuous interaction with the customer • Several refinements: PP, SCRUM, …

13. A typical industry project in 1990 New code C standard library Home-grown library

14. Same industry project NOW New code Home-grown library Commercial libraries for industry segment IDE modules Open source components C++/Java standard library Follow-up Q: Any disadvantages?

15. Questions • Expert programmer: what does it mean? • Future projects: No code? Just integration? • Knowledge of Java/C++/… down the drain?

16. Application Specific Programming script script script … Intrepreter M1 M2 M3 Mn …

17. Example: Casio FX100 calculator in PC

18. Cloud Computing • SW bundles inefficient for small companies • Too much IT equipment & staff overhead • Cloud resource provider provides the SW over the web and stores data too – SaaS model • Each client needs only web browser and internet access.

19. Analogy: making food • Make Sambar from scratch • Make Sambar using powder • Make Maggie Noodles • Microwave frozen sambar / Tetrapack • Inefficient for singles – make food in one place & get it delivered?

20. Data Exchange A2 A1 A4 A3

21. Data Exchange • Binary or text data  XML or JSON … • Independent data checkers tools available. A2 A1 A4 A3

22. Software Outsourcing • Goal: reduce cost • Projects need to fit certain criteria • Bit formal & clear requirements • Interfaces well defined/understood • should be of reasonable size • Not a good idea for small/poorly defined/time-critical/security-sensitive projects • Opinion: may stay at current level or go down • Industry not conducive for more outsourcing • With increased cost, Indian SW companies under pressure

23. Design decisons • Purchase modules or use open-source? • .NET or Java?

24. Running time of a program? • size of input (n) • CPU speed • memory size & speed • actual processing, time complexity of algorithm • compiler optimization, dev execution environment • …

25. Compare these algorithms

26. Time complexity – our goal

27. Running time improvements If a program takes 1 week to complete, how will you approach to reduce running time?

28. Software efficiency: Do we really care? • Goal is to make software work & meet deadline for most industry projects. Let us consider a few examples: • Game console: Algorithm takes longer to run  requires higher-end CPU to keep realism  higher price  product fails amid competition

29. Software Efficiency: Web server • Consider 2 algorithms A1 and A2 that take 5 seconds & 20 seconds per transaction. • web-user experience? • web server capacity? A1 A2 Internet Internet User1 User2

30. Daily data crunching: What if it takes >1 day? • Equation for running time = c1. n + d1 • Time complexity is O(n) • initial setup • loop • read one tuple • open db connection • send request to db • get response from db • close db • post-processing

31. Data crunching pseudocode • Equation for running time = c2. n + d2 • Time complexity is still O(n), but the constants are different. • c2 < c1 • d2> d1 • initial setup • open db connection • loop • read one tuple • send request to db • get response from db • close db • post-processing

32. Wireless node capacity • BHCA capacity: 10 lakhs calls per hour • Each call requires (1 hour * 70%/(10 lakhs)) • = 2.5 milliseconds. • If that 2.5 msec becomes 3.0 msec due to additional processing, new capacity will be • 10 * 2.5 = x * 3.0  x = 8.3 lakhs BHCA • 17% capacity reduction

33. Data download & processing open web browser loop click & download next file process file done! • Let us say both steps take 5 minutes each & we have 10 files to process. • What is the total processing time? • How can we optimize? • Hint: download uses I/O and processing uses CPU.

34. Data download & processing open web browser loop click & download next file process file until all files are processed • Let us say both steps take 5 minutes each & we have 10 files to process. • What is the total processing time? • > 10 * 10 = 100 minutes • How can we optimize? • Hint: download uses I/O and processing uses CPU. • Real-world example? • Making chappathis?

35. Data download & processing open web browser click & download next file loop click & start to download next file process previous file until all files are processed • Download & processing in parallel. • What is the total processing time now? • > 5 * 10 = 50 minutes • More complex algorithm 

36. A few more points … • Standalone applications  Enterprise bundles • Local SW installation  intranet based • Build in-house  Buy & customize • Heavy text based coding  visual programming (GUI builders & debuggers …) • Desktops  Laptops + Smartphones

37. SW projects: Multi-dimensional problem Companies want to complete Software Projects with • Smaller team • Limited resources • Higher quality • Reduced cost • On-time delivery This is what each project manager tries to do 

38. Smart-phone development • My kids know/play games in Android phone • For me, email, calendar and contacts are in sync with company data • Question: can we do more with them? • Industry trend is to port several meaningful business applications to smartphones. • Example for cross-platform development

39. Game development • Lot of interest in real-time action games • Most games done in C/C++ in Los Angeles area • Algorithm optimization at work!

40. Latest trends in CS education in USA • Difficulty in attracting US students to CS • Introduction to Programming : going visual • Alice, Scratch, GUI games, … • Java dominates in CS1 & CS2 courses • Industry projects in the final year • Online courses becoming popular, augmenting education landscape • Lots of online multimedia lectures in the web • Game design programs & Smart-phone applications • Augmenting open-source applications

41. Dallas-Ft. Worth metroplex

42. Advertisement:University of Texas at Dallas • Erik Jonsson School of Engineering and Computer Science • Computer Science: ~500 MS students and ~150 PhD students • Surrounded by 100s of companies in Dallas-Fort Worth metroplex • Students can get internships right after 2 semesters and continue studies in parallel • Flyers available – see me after the lecture

43. Questions & Answers

44. Thanks for attending! Dr. Jeyakesavan Veerasamy jeyak7@gmail.com jeyv@utdallas.edu