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PASTE 2001 Making Slicing Practical: The Final Mile William Griswold University of California, San Diego in collaboration with Leeann Bent (UCSD) & Darren.

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Presentation on theme: "PASTE 2001 Making Slicing Practical: The Final Mile William Griswold University of California, San Diego in collaboration with Leeann Bent (UCSD) & Darren."— Presentation transcript:

1 PASTE 2001 Making Slicing Practical: The Final Mile William Griswold University of California, San Diego in collaboration with Leeann Bent (UCSD) & Darren Atkinson (Santa Clara University) special thanks to GrammaTech Inc.

2 2 The Conundrum Program slicing has been an archetype of SE analysis for 20 years [Weiser 80] –reveal hidden or dispersed program relationships –assist in reasoning about and changing them Yet program slicers are not widely used Why not? –Then: implementations were too slow and imprecise –Now: inadequate attention to essential SE needs

3 3 Static Backward Program Slice void setg(int a1, int a2) { int x = 1; g = a1; if (pred(a2)) { g = x; } printf(%d, g); } Set of expressions and statements that may affect the value of a chosen variable reference during execution. [Weiser 81] Background initial slicing criterion

4 4 Benefits & Applications of Slicing –Debugging: What subset of statements might have helped produce the wrong value for g ? –Evolution: What components are inputs to the one Im evolving? Does this change break that feature? –Testing: I changed this statement, what subset of the tests do I need to rerun? Key value is its subsetting property: helps a programmer focus on the parts of the system relevant to current task Underlying dependences are also of potential value

5 5 Freddie Krueger, Tool User Your tool can solve all sorts of problems for us. But have to analyze our entire 1 MLOC program, which is written in 4 languages and doesnt compile right now. I want the results as fast as compilation, with an intuitive graphical display linked to the source and integrated into our IDE. I want to save the results, and have them automatically updated as I change the program. By the way, I use Windows, but some of my colleagues use Unix. Its OK if the tool misses stuff or returns lots of data, as long as we can post-process. We just need a net win. Your tool can solve all sorts of problems for us. But itll have to analyze our entire 1 MLOC program, which is written in 4 languages and doesnt compile right now. I want the results as fast as compilation, with an intuitive graphical display linked to the source and integrated into our IDE. I want to save the results, and have them automatically updated as I change the program. By the way, I use Windows, but some of my colleagues use Unix. Its OK if the tool misses stuff or returns lots of data, as long as we can post-process. We just need a net win. Our most recent study involved a 500 KLOC Fortran/C app developed on SGIs

6 6 grep Slicer Fast, scalable Easy to use Flexible (caps, words…) Language independent Integrated in every env. Imprecise –Allows iteration & filtering Used in many activities –Renaming, restructuring, finding change points… Slow, doesnt scale Easy to use Forward, backward, chop C; Java coming Stand-alone Precise, but slices large –Allows iteration & filtering Several postulated uses ?

7 7 Our Experience on the Final Mile Several years designing Sprite, a fast, scalable program slicer for C Comparative study [2000] with GrammaTechs CodeSurfer slicer for C, a commercial product Other tools: design, implementation, and user studies

8 8 Sprite Slicing Criterion

9 9 CodeSurfer 1.1p1

10 10 Why Were Still on the Final Mile Little understanding of programmers needs, tasks, or how they would use a slicer Slicers suffer from inflexibility & poor usability User interfaces are in formative stages Several opportunities insufficiently explored

11 11 Usability and Flexibility Wrote few dozen feature benchmarks –Unstructured c-flow, context sensitivity, pointers... –Expose intervening, interacting algorithmic factors 5 small production-quality programs –compress, wally, ispell, ed, diff A few larger programs using Sprite –gcc, emacs, FEM app Varied slicer options, studied results Part 1

12 12 Uninitialized Pointers int *p; // uninitialized... *p = x; y = *p; Neither tool included x in the slice No warning Misleading (say, if youre debugging)

13 13 Undefined Functions x = f(&y,z); Sprite: included call, but did not propagate CodeSurfer: also propagated to &y and z –No propagation to z if slicing on y Both printed warning to terminal (not GUI) –Easy to not notice –Easy to forget a library or have undefined function

14 14 Library Modeling Both modeled effects of libraries with skeletal functions int write(int fd, char *s) { return (*filesystem[fd]++ = *s); } Only libc and libm, both incomplete –Only a few undefined functions for our programs –CodeSurfers noticeably better, more complex Missing could impact precision, effort, or perf. –What if need Xlib???

15 15 Slicing into Callers setg(a, b);... } void setg(int a1, int a2) { int x = 1;... Sprite: could not slice into callers CodeSurfer: must slice into callers Neither customizable to produce others results (CodeSurfer did support single-step slicing and slicing on just control- or data-dependences)

16 16 Control Dependence Sensitivity Spurious dependence can bloat slice by x10 –Indirection through array of function pointers Sprites strongly typed function calls feature fixed gcc problems, not emacs –Modelling of control dependences (CodeSurfer) switch (ch) { case a: if (pred(e)) return; g = x + 1; break; case b: g = x + 2; break; } FuncType ops[N] = {&f1, &f2, …}... (*ops[i])(a1, a2);

17 17 Statement Inclusion & Highlighting Sprite: no global decls, uninitialized locals, control flow keywords –Included goto s only if option enabled CodeSurfer: executable slice highlighting –Includes syntactic sugar Right choice depends on users task – –Omitting a statement could lead to oversight – –Overwhelming with highlighting can, too

18 18 Understanding Slicing Results Hard: Why is this statement in the slice? –Forgotten or incomplete libraries –Style of slice (into callers, declarations) –Real dependence or algorithmic artifact? –Control, data, pointer,... –Gap betw. highlighting & underlying dependences Querying dependence edges, points-to sets unhelpful Answer critical to correct software change We modified: comment out pointer assignment –Must rebuild, reslice, and compare results by hand

19 19 Summary - The Bad News Tools had hidden behaviors on erroneous or incomplete programs –Could mislead programmer, hampering use Tools had rigid notion of a slice –Each suited for some tasks, but not others Tools required significant effort to use –Completeness of libraries –Understanding reasons for inclusion in slice Implementations of algorithms, not full tools

20 20 Summary - The Good News Both tools supported (potentially) inaccurate analyses to remove uninteresting information –Sprite: user customization of pointer properties –CodeSurfer: I/O functions that arent interdependent –Suggests generalizing slicing as an SE analysis –Unfortunately, manipulating analysis can be costly Variance between tools exposes tool options –Suggests user-customizable features –E.g., highlighting options: decls versus no-decls

21 21 The Human–Computer Interface Good user interfaces are crucial to effectiveness –Leverage & aid programmers cognitive processes –A key difference between an algorithm and a tool Example for evolution (what I understand) –Tremendous scale –Wide dispersal of information: redundancy, many references to an element –Requirement for complete and consistent change of the dispersed elements (e.g., rename) –Invisibility of far-away information stresses recall Part 2

22 22 Easy to use and fast –Takes a textual pattern and target for search grep cursor *.java –Lists all matching lines and highlights in editor –Next/Previous operations traverse the matches Scalability issues: limited visibility, inflexible –Can see just a few matches in context of use –One search at a time; cannot compare results Example - grep & Company

23 23 Highly evolved artifact for coping with scale –Spatial organization, zooming, cursors, insets, indices, folding, itineraries Idea: augment grep with Seesoft-like view –Delineated regions to represent files –Colored symbols for matched lines in files –Puts matches on an equal footing with modules See only portion of program, little of value Use map metaphor fully: Aspect Browser Maps Enhance Visibility Lines with two or more matches Lines matched by pattern File strips See only portion of program, little of value Use map metaphor fully: Aspect Browser

24 24 You are here Folding cue that files are hidden Views updated on save Now extending with Atlas metaphor to accommodate vast scale

25 25 CodeSurfer GUI is half-way there One-file Seesoft summary Traversal operations Cue of underlying hits

26 26 Complements Slice explainer –High-level analysis of why statement is in slice May/Must highlighting –Overlay slice with highlighting dynamic slices or execution coverage –Uncovered stmts, dependences hint at imprecision Filtering w/ other analyses and customizations –CodeSurfer has chopping; Sprite set operations –grep, Ajax tools [OCallahan] –PIM-like customizations [Field] With small run-time impact Part 3: Opportunities

27 27 Emerging Challenges Multi-threading [Hatcliff et al.], exceptions –intrinsically flow-sensitive, so flow-insensitive pointer analysis is substantial loss [Rugina] What is a program? What is the program? –Federated client-server apps Often multi-language –Trend is to write component glue Less source code, but huge apps How treat vast, numerous packages? Sans source? –Analysis via Java library byte codes costly [OCallahan] –Vast amount of stub code to write for libraries

28 28 Long-Standing Issues Robustness - incomplete or buggy programs –Useful for developing and evolving systems –Complicates the analysis –Harder for programmer to interpret results Integration into IDEs [Hayes 2000] –Work with IDEs GUI and AST, or else bloat –Reuse analyses across IDEs In principle not hard In practice, performance/precision tradeoffs can require big rewrites for small language change

29 29 Recommendations & Future Work Explore non-algorithmic aspects of precision Attend to users tasks and habits –Giving users what they need is precision Do an observational study of programmers –Essential problems: complex tasks of programmers –Accidental problems: kludgy user interfaces… Listen to Michaels talk tomorrow ;)

30 30 Recap 20 years of slicing research –Huge strides in algorithmic precision & performance –Little adoption Non-algorithmic factors like libraries have significant impact on precision –Cannot ignore, now that libs dominate applications Remaining problem is usefulness of slicers –Confusing behaviors, inflexibility, and need for costly programming are barriers to adoption –Understanding programmers use of dependences


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