1. Code search & recommendation engines
A summary of some results from research
Robert Feldt

2. Overview
Lots of research on Code Search & Recommendation Engines in last 5-10 years
This is quite a general overview of some relevant results; you already know some of it
Recent trend is Program Repair based on code search (history- or semantics-driven)

3. Goal is to make developers more effective
We want them to more efficiently:
Reuse code that already does what they need
Find similar code they can change
Find examples of how to use similar code
Find test code for related code
Help them adapt the code
Find similar code, doc & bugs for fixing new bugs
Help them automatically repair code (fix bugs)

4. Recommendation engines
“Recommendation systems are software applications that aim to support users in their decision-making while interacting with large information spaces. They provide information items estimated to be valuable for a software engineering task in a given context.”
Proactively tailor suggestions to meet developers (& testers) current needs and preferences
Help find right & relevant info when developers:
Lack experience
Can’t consider all relevant info at the same time
Important, basic/overview work:
[Robillard2010] M. Robillard et al ,”Recommendation Systems for Software Engineering”, IEEE Software, 2010.

5. RSSE design dimensions

6. Portfolio: Finding Relevant Functions and their Use

7. Portfolio Interface

8. Portfolio: Evaluation Results
Portfolio was better than Google Code Search and Koders engines
Higher confidence, precision and gain
Improves ranking of functions: higher in list
Half of relevant functions did not contain keyword from Q
Call graph analysis required, keyword search not enough
Needed both Keywords, PageRank and SAN algorithm for best results

9. Active Code Search: Improve Ranking based on Feedback
Wang, Shaowei et al, “Active Code Search: Incorporating User Feedback to Improve Code Search Relevance”, ASE ’14, 2014, Västerås, Sweden.

10. Active Code Search: Results
10-15% better than Portfolio (PageRank + SAN)
They use it online for one user but
Can also be saved for future use
Nice way for you to get this information back from plug-in:
Add REST function:
rerank_result(resultID, [(resultNum1, “NotInteresting”), (resultNum2, “Interesting”)])
it returns a new result, and
saves the feedback to improve future search
It is a general solution regardless of how you produce result lists, feedback can always improve it

11. Mining StackOverflow
Ponzanelli et al, “Mining StackOverflow to Turn the IDE into a Self-Confident Programming Prompter”, MSR’12, 2012.

12. PROMPTER: Architecture
Ponzanelli et al, “Mining StackOverflow to Turn the IDE into a Self-Confident Programming Prompter”, MSR’12, 2012.

13. Repairing bugs based on semantic code search
SearchRepair:
Semantic Code Search = Encode/index based on its constraints on input-output behavior
Localize a defect to buggy part of program
Construct input-output profile for buggy code
Search database for fragments with similar profiles
Generate patch based on similar code and validate against test suite
Results:
Repaired programs passed 97.3% of tests while other techniques reached only 64-72% correctness
Quite involved technique since it involves constraint solving
Ke et al, “Repairing Programs with Semantic Code Search”, ASE’15, 2015.

14. Thank you!