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Automatic High-Level Action Detection in Methods Presentation by: Gayani Samaraweera

This research focuses on automatically detecting and describing high-level actions within methods by analyzing code fragments and expressing them in natural language. Techniques include syntax tree analysis, linguistic knowledge, and SWUM word usage. Challenges arise in integrating similar statements, abstracting conditionals, and identifying loops. Evaluations show significant reductions in identified high-level actions with improved precision.

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Automatic High-Level Action Detection in Methods Presentation by: Gayani Samaraweera

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  1. Automatically detecting and describing high level actions within methods Presented by: GayaniSamaraweera

  2. The problem • Given signature and the body of a method M, automatically discover each code fragment that implements a high level action comprising the overall algorithm of M, and accurately express each high level action as a succinct natural language description

  3. Outline • The problem • Outline • High level actions • Method • Evaluation • Concerns • Other uses • Conclusion

  4. High level actions • Sequence fragment • A sequence of statements that when taken together represents a single high level action • Conditional fragment • A conditional block that performs an action with subtle variations based on the condition • Loop fragment • Code patterns that are commonly implemented using loop constructs that constitute a high level action

  5. Method

  6. Detecting high level actions • Uses • AST (Abstract Syntax Tree) • CFG (Control Flow Gragh) • Information from naming conventions and linguistic knowledge gained from observations of Java programs • Textual clues from SWUM (Software Word Usage Model)

  7. cont.. • Word usage information from identifiers • Identifier splitting • Camel case splitting: on capital letters, underscores, numbers • Eg: childXMLElement → child XML Element • Expand identifier abbreviations • Eg: Button butSelectAll, MouseEvent evt • SWUM → action, theme, optional secondary arguments of a statement grouping • Eg: list.add(Item i); → “additemto list” action secondary argument theme

  8. Sequence as single action • Sequence fragments • Identifying sequences of statements with similar actions • Indicated by similar method calls

  9. cont.. • Challenges • Integrate to successor statement based on similarity • Different method names • Same method name different parameter types

  10. cont.. • Identifying fragments • Statements with one or more method calls Add ended panel to content panel Add bid panel to content panel Verb → add → equals Head word of NP → panel → equals Preposition → to content panel → equals → integratable

  11. cont.. • Synthesizing descriptions • If equal head word → plural • Else Add okButton to content panel Head word of NP → different But if fields of same class → “all attributes” “different attributes”

  12. Abstracting conditionals • Challenges • Integrating similar statements in different branches • Integrating conditional statements guarding different branches • Integrating return statements with literals or similar method calls

  13. cont.. • Identifying and describing conditionals • Integrate statements of each block, compare each statement with statements of parent block • For method calls Singular

  14. cont.. • For return statements • For assignment statements Theme based on enclosing method Update, create or get

  15. cont.. • Describing conditional expressions • Compare phrases as 'subject predicate object' • Subject and predicate are equal → based on what <subject> <predicate> Based on what os name starts with If only head word of subject is equal Based on which <head word> <predicate> <object> Based on which radio button is selected

  16. Finding traceable patterns in loops • Challenges • Common algorithms as finding, counting, copying • Develop identification templates • Develop heuristics to synthesize phrases for each template

  17. cont.. • Loop abstractions implemented • Count • Contains • Find • Copy • Max-min

  18. cont.. • Identifying fragments and synthesize templates

  19. cont.. • Variations in synthesis templates • 'find item (in collection) whose/which/such that <criteria>' in subject predicate object, • If item is subject → which • If an attribute of item is subject → whose • Default → such that

  20. Evaluation • Executed on 1.2 million methods across 1000 Java programs

  21. cont.. • How prevalent are the implemented high level methods? • Sequence (methods with >= 10 statements 12.5%) • 11% • Conditional • 40% of if-else • 24% of switch • Loop • 51% of loops classified as iterating over all items in a collection • 15% of iteratorloops detected by implemented patterns

  22. cont.. • Potential reduction in reading detail • Reduction in identified high level actions • Sequence → one phrase • 22% of original size • Conditional → two phrases • 29% of original size • Loop → varying # phrases • 25% of original size

  23. cont.. • Precision of identification and description • 15 human evaluators, each evaluating 15 code fragments (5-sequence, 5-conditional, 5-loop) • 75 code fragments from 15 projects evaluated by 3 evaluators From methods with <= 20 statements 25-conditional, 25-sequence, 25-loop Loops: 5 fragments from each 5 patterns

  24. cont.. • Evaluators wrote an abstraction of the method • Answered following based on 1 – strongly disagree to 5 – strongly agree identification description

  25. cont.. Majority agreed or strongly agreed on both P1 and P2

  26. Concerns.. • May not generalize to other Java programs • Results may vary on larger programs • Results might not hold with novices • Reduction in reading measurement may not hold with some developers

  27. Improving client tools • Extract method refactoring Create application based on what os starts with Set different attributes of SVGApplicationModel

  28. cont.. • Internal comment generation • Instead of Extract Method refactoring, can add comments inline • Add empty lines between related code fragments • Suggesting more informative method names • Improving automatically generated summary comments for a method

  29. Conclusions • First technique for identifying code fragments of statement sequences, conditionals and loops, that is abstracted to a high level action • Automatically synthesizing natural language description

  30. References • GiriprasadSridhara, Lori Pollock, and K. Vijay-Shanker. Automatically detecting and describing high level actions within methods. In Proceeding of the 33rd international conference on Software engineering (ICSE '11). ACM, New York, NY, USA, 101-110. • G. Sridhara, E. Hill, D. Muppaneni, L. Pollock, and K. Vijay-Shanker. Towards Automatically Generating Summary Comments for Java Methods. Intl. Conf on Automated Softw. Engg. (ASE’10), 2010. • GiriprasadSridhara, Lori Pollock, K. Vijay-Shanker, "Generating Parameter Comments and Integrating with Method Summaries," International Conference on Program Comprehension, pp. 71-80, 2011 IEEE 19th International Conference on Program Comprehension, 2011 • E. Hill. Integrating Natural Language and Program Structure Information to Improve Software Search and Exploration. PhD Dissertation, University of Delaware, 2010.

  31. Thank you for listening!

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