Function Inlining Algorithm for Program Analysis

Wang, Tiantian; Su, Xin; Ma, Peijun

doi:10.1109/cise.2009.5364393

Cited by 1 publication

(2 citation statements)

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“…Step 1: removal of problematic code such as syntax error, expression error, and any other with tag problem in ASTdata by problem binder which processed while if (ANode, BNode) � (ATag ANodeNo, BTag BNodeNo) (13) TagMap ⟵ TagMap ∪ {ATag ↔ BTag} (14) LocalNodeMap ⟵ LocalNodeMap ∪ {ANodeNo ↔ BNodeNo} (15) else if (ANode, BNode) � (AChildNo :� a op a', BChildNo: � b op b') (16) SimilarityMatching(a, b) (17) SimilarityMatching(a', b') (18) if isLocal(a) and is Local (b) and TagMap(a, b) is equal (19) LocalNodeMap ⟵ LocalNodeMap ∪ {a ↔ b} (20) ns+1, cp+1 (21) else if (22) GlobalNodeMap ⟵ GlobalNodeMap ∪ {a ↔ b} ( 23) cp+1 (24) else if. .…”

Section: Program Standardizationmentioning

confidence: 99%

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Automatic Grading for Complex Multifile Programs

et al. 2020

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This paper presents an automatic grading method DGRADER, which handles complex multifile programs. Both the dynamic and the static grading support multifile program analysis. So, it can be an advantage to handle complex programming problem which requires more than one program file. Dynamic analysis takes advantage of object file linker in compilation to link complex multifile program. The static grading module consists of the following steps. Firstly, the program is parsed into abstract syntax tree, which is mapped into abstract syntax tree data map. Then, the information of preprocessor is used for linking external sources called in main program by complex multifile program linker-fusion algorithm. Next, standardization process is performed for problematic code removal, unused function removal, and function sequence ordering based on function call. Finally, program matching successfully tackles structure variance problem by previous standardization process and by simple tree matching using tag classifier. The novelty of the approach is that it handles complex multifile program analysis with flexible grading with consideration of modularity and big scale of programming problem complexity. The results have shown improvement in grading precision which gives reliable grading score delivered with intuitive system.

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Section: Program Standardizationmentioning

confidence: 99%

“…* regex ⟵ ″¥″([^¥″] * )¥″″(8) while patternMatch (regex) � � true(9) add to pfname[i](10) Step 3: Link external sources using mp[] to get pfname[] (11) for i � 1 to mp[] size(12) for j � 1 to pfname[] size(13) if(mp[i] contains pfname[j]) fpath ⟵ mp[i] (14) if (j � � 1) fmp ⟵ fmp concat (mp[i])…”

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confidence: 99%