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A Regression Test Selection Technique for Aspect-Oriented Programs

A Regression Test Selection Technique for Aspect-Oriented Programs

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A Regression Test Selection Technique for Aspect-Oriented Programs

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  1. A Regression Test Selection Technique for Aspect-Oriented Programs Guoqing Xu The Ohio State University xug@cse.ohio-state.edu

  2. Outline • background • Problem statement and motivation • Our analysis • Implementation status • Related work

  3. Regression Test Selection • Testing after software modifications • Re-running the entire regression test suite is expensive • Select a subset of tests to run • Safe test selection chooses every test case that may reveal a fault

  4. Related Previous Work • Rothermel and Harrold, TOSEM 97 • Graph traversal algorithms • Harrold et al., OOPSLA 01 • Java interclass graph (JIG) Program P Execute P/ Record Coverage Program P’ P’s Edge Coverage Matrix Select Tests Dangerous Entities containing edges in P Identify Dangerous Entities Program P Program P’ Copied from [Harrold et al., OOPSLA 01]

  5. Regression Testing for AOSD • When AO features are added/modified, the program needs to be regression tested • Case 1: P is an OO program and P’ is an AO version of P • Case 2: Both P and P’ are AO programs • How should regression test selection be performed for AO software?

  6. Outline • Background • Problem statement and motivation • Our analysis • Implementation status • Related work

  7. Existing Work Applied to AO Programs • How to compare two JIGs? • The JIG of the woven code includes redundant nodes and edges and does not correspond to the logical control flow as presented in the source • Need new representations • How to recover CFG edges from the execution trace when computing edge-coverage matrix? (when P is AO program) • The execution trace is compiler-specific • Need instrumentation before/during weaving

  8. bar() C.m() p.m() … return exit exit Existing Work Applied to AO Programs • class C { • public void m(int i) {…} • 3. } • void bar(C p, int k) { • p.m(k); • } • Java version P • CFG edge • Call edge Java Interclass Graph (JIG)

  9. bar() bar() ..Sample.around$0 ..around$0 C.m() … p.m() return … c.m() C.m () Return … return exit exit JIG for P’ (from woven code) JIG for P Example • class C { • public void m(int i){…}; • 3. } • void bar(C p, int k) { • p.m(k); • } • aspect Sample{ • void around(C c, int i): • call(C.m(int)) && • target(c) && • args(i) { • 14. proceed(p , x); • 15. } AspectJ Version P’

  10. Some Results • When we applied the [Harrold et.al. 01] algorithm to several subjects:

  11. Possible Approach • Create “clean” CFGs in which the wrapper code inserted during weaving is removed • Graph traversal and comparison corresponds to the “logical” structure of the code, not the compiler-specific woven code • New representation: AJIG • AspectJ Inter-module Graph – more later • For regression test selection, need to consider additional issues

  12. bar() System.out…. m() C.m () return … exit exit AJIG for P’ • Do we need to select all the tests that go through the edge marked in red? A More Complex Problem • class C { • public void m(int i){…}; • 3. } • void bar(C p, int k) { • C.m(k); • } • aspect Sample{ • void around(C c, int i): • call(C.m(int)) && • target(c) && • args(i) { • System.out.println(i); • proceed(p , x); • 15.}

  13. Why it needs to be addressed • This is an issue not only for AO software, but also for procedural and OO software • Advices are often free of side effects • Study in [Rinard et al. FSE 04] reported 6 “observer” advicesout of ten inspected advices • Recommended for “safe” AO programming • Adding side-effect-free advices should not result in overly conservative regression test selection • Approach: use side-effect analysis

  14. Outline • Big picture and background • Problem statement and motivation • Our analysis • Implementation status • Related work

  15. Our Work • Consider both situations: • Case 1: P is an OO version, P’ is an AO version • Case 2: both P and P’ are AO versions • Analysis to select regression tests • Build a new control flow representation: AJIG • Apply existing graph-traversal algorithm onAJIG • Side-effect analysis when comparing AJIGs

  16. AJIG • AspectJ Inter-module Graph (recent work) • For the Java parts, same as JIG. • Shadow node • A shadow node is associated with • a set of JIGs of advices • the precedence of these advices • an integrated shadow advice JIG • AJIG supports allstatic AspectJ pointcut types • Conservatively approximate the dynamic part of pointcut designators.

  17. Basic Idea • Build AJIGs for P and P’. • Apply the graph-traversal algorithm on AJIGs of P and P’.

  18. New Test Selection Criterion • Side effect related node in AJIG • Has side effects • Has some dependency on the nodes that have side effects • Safe edge in AJIG • An edge is safe edge, if the sink node of this edge is side effect related.

  19. New Test Selection criterion • New Test Selection criterion: • Dangerous set S computation: for each edge e in P, and its counter part e’ in P’ • e is not equivalent to e’and • both e and e’ are side effect related • Select a test that execute one or more edges in S

  20. Selection • Computing dangerous set S by comparing AJIGs. • We plan to use some form of side-effect analysis • Large body of existing work • Selecting tests.

  21. Implementation progress • The implementation of algorithms described in [Harrold et.al. 01] √ • Building AspectJ Inter-module Graph √ • Make an extension for the abc compiler that generates the Jimple based CFGs for aspects between the weaving of inter-type declarations and advices. • Instrument advices at different phases. • Side effect analysis ongoing work • Evaluation

  22. Related Work • Static/Dynamic Analysis for AO programs. • abc compiler [AOSD 05] [PLDI 05] [TR 04]. • Static analysis of aspects [Sereni and Moor, AOSD 02]. • Zhao’s work on the analysis and testing of AO programs [COMPSAC 03] [WPC 02][AOSD 06]. • Josh-an open implementation of AspectJ-like languages [Chiba and Nakagawa AOSD 03] • Classification system for AO programs [Rinard et. al. FSE 04].

  23. Related Work (Cont’d) • Regression Test Selection • [LW ICSM 91], [CRV ISCE 94], [RH TOSEM 97], [Ball ISSTA 98], [Harrold+ OOPSLA 01], [OSH FSE 04]… • Change Impact Analysis • [KGH+ ICSM 94], [RT PASTE 01], [OAH FSE 03], [OAL+ ICSE 04], [RST+ OOPSLA 04], …

  24. Thank you!! Questions??