to Grammarwaregrammarware.net/slides/2014/compilers2grammarware.pdfFor example, binary operators...
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to Grammarware Dr. Vadim Zaytsev From Compilers
Transcript of to Grammarwaregrammarware.net/slides/2014/compilers2grammarware.pdfFor example, binary operators...
to GrammarwareDr. Vadim Zaytsev
From Compilers
Introduction Compilers Grammarware
Transformation ConsistencyMaturity
Understanding Testing Conclusion
Introduction
• Vadim Zaytsev
• MSc in appl.math (2003) & telematics (2004)
• PhD in softw.lang.eng. (2010)
• Postdoc at CWI (2010–2013)
• Lecturer at UvA (2013–…)
What is a compiler?
Language processing
• Internal structures
• databases, configurations, tables, …
• External structures
• protocols, interfaces, bytecode, …
• Software language
• programming, modelling, markup, …
Compiler
Front End
MIDDLE End
BACK End
Multi-language compiler
Front End
MIDDLE End
BACK End
Front End
Front End
Multi-target compiler
Front End
MIDDLE End
BACK End
Front End
Front End
BACK End
BACK End
Grammarware
Front End
MIDDLE End
BACK End
Front End
Front End
BACK End
BACK End
Compilers transform between languages
Grammarware commits to grammatical structure
Kinds of grammarware
• Parser • Compiler • Interpreter • Prettyprinter • Scanner • Browser • Static checker • Struct.editor
• IDE • DSL • Preprocessor • Postprocessor• Validator• Model checker • Refactorer • Code slicer
• API• XMLware • Modelware • Lang.• RE • Benchmark • Recommender • Renovation tool
Klint, Lämmel, Verhoef, Toward an Engineering Discipline for Grammarware
Declarative Multi-Purpose Language Definition
Syntax Definition
Name Binding
Type Constraints
Dynamic Semantics Transform
Languages vs. grammars
Visser,
Introduction Compilers Grammarware
Transformation ConsistencyMaturity
Understanding Testing Conclusion
What is good grammarware?
Case study: JLS
?Lämmel, Zaytsev, Recovering Grammar Relationships for the
What is good grammarware?
What is good software?
What is good software?
• functional
• reliable
• usable
• efficient
• maintainable
• portable
ISO/IEC 9126.
What is good grammarware?
• functional: commits to the language
• reliable: tolerant to errors
• usable: the language is learnable
• efficient: fast (live?) and responsive
• maintainable: can be tested and evolved
• portable
Certified Language Processor
Certified Language Engineer
Capability Maturity Model
• Level 1 — Chaotic
• Level 2 — Repeatable
• Level 3 — Defined
• Level 4
• Level 5 — Optimising
Paulk, Weber, Curtis, Chrissis, Capability Maturity Model for Software
Grammar Zoo
• 974 fetched grammars
• 588 extracted
• 79 connected
• 9 adapted+metadata
http://slebok.github.io/zoo
Zaytsev, Grammar Maturity ModelZaytsev, Grammar Zoo: A Corpus of Experimental Grammarware
Improving quality
• Manual inline editing
• Refactorings
• Programmed transformations
• +Differs
• Grammar mutations
• Inference of transformation/mutation steps
How to transformexpr : …; atom : ID | INT | '(' expr ')';
expr : …; atom : ID; atom : INT; atom : expr;
expr : …; expr : ID; expr : INT; expr : expr;
expr : …; expr : ID; expr : INT;
expr : …; atom : ID | INT | expr;
abstractize
vertical unite
abridge
Lämmel, Zaytsev, An Introduction to Grammar Convergence, IFM’
• Grammar has no starting symbol?
• Reroot2top
• Need abstract syntax from concrete syntax?
• RetireTs
• Grammar productions written in an
• DeyaccifyAll
• Change naming convention?
• RenameAllNLower2Camel
How to mutate
Zaytsev. Software Language Engineering by Intentional Rewriting, SQM’14
How to be guided• Equality & algebraic equivalence
• Prodsig-equivalence
• signatures based on nonterminal patterns
• tolerant to permutations
• weak equivalence tolerant to iteration kinds
• Abstract Normal Form
• no terminals, labels, markers
• consistent disjunctive style
Zaytsev, Guided Grammar Convergence,
How to be guided
p
master
= p(", expr , expr · operator · expr)F p
antlr
= p(", binary , s(l, atom) · ⇤(s(o, ops) · s(r, atom)))F p
dcg
= p(binary, expr , atom · ⇤(ops · atom))p
emf
= p(", Binary , s(ops,Ops) · s(left,Expr) · s(right,Expr))p
jaxb
= p(", Binary , s(Ops,Ops) · s(Left,Expr) · s(Right,Expr))p
om
= p(", Binary , s(ops,Ops) · s(left,Expr) · s(right,Expr))F p
python
= p(", binary , atom · ⇤(operators · atom))p
adt
= p(", FLExpr , s(binary, s(e1,FLExpr) · s(op,FLOp) · s(e2,FLExpr)))p
rascal
= p(binary, Expr , s(lexpr,Expr) · s(op,Ops) · s(rexpr,Expr))p
sdf
= p(binary, Expr , Expr ·Ops · Expr)p
txl
= p(", expression, expression · op · expression)p
xsd
= p(", Binary , s(ops,Ops) · s(left,Expr) · s(right,Expr))
Fig. 2. Production rules from di↵erent grammars of the same intended language,representing the same intended construct. Black stars show the production ruleswith iterative implementations. p
master
is p9 from Table 1.
As we can see, there are two layers: one for atomic expressions such as numbersand names, which connects to the layer of more elaborate expressions that alsorefer to it. Both atom and expr belong to the same grammatical level [15] and aretightly connected, but separated for the reasons of technical nature: typically,the lack of mechanisms for expressing priorities explicitly.
Definition 12. Nonterminals ni 2 N , i = 1, 2, . . . , k are expression layers, if
1. for each i < k there is at least one production rule pi that reflexively refer-ences ni and at least one production rule qi that references ni+1
2. there is at least one bracketing production rule qk that defines nk and refer-ences n1,
where qk is a chain production rule modulo terminal symbols and named subex-pressions.
Def. 12 can be made even more specific if we know what kind of operatorsbetween adjacent layers we want to detect. For example, binary operators willlead to right hand sides in the form of ni+1 · ⇤(op · ni+1) or ⇤(ni+1 · op) · ni+1,which can also be contransformed in order to respect associativity.
The definition of expression layers has a straightforward algorithmic repre-sentation that we have specified as a metaprogram that detects the presence oflayers, merges (unites) the nonterminals into one. Automatic removal of a result-ing reflexive chain production rule is not always possible (or, strictly speaking,not always desirable), due to the presence of terminals and selectable subexpres-sions, so we leave it there until the normalization phase.
6.3 Associativity
As we have mentioned in the previous section, associativity in the presence oflayers is expressed iteratively. To illustrate this, let us take Figure 2 to list
Zaytsev, Guided Grammar Convergence,
What we want in general
• Maintenance assistants
• infer whatever possible
• provide advice on the rest
• Not necessarily “request => result or fail”
• pending
• negotiated
Zaytsev, Pending Evolution of GrammarsZaytsev, Negotiated Grammar Evolution
Negotiating the resultrename(expr,Expr)
ok
no expr!
rename(exp,Exp)
Zaytsev, Negotiated Grammar Evolution
Key points• For grammarware, we need
• consistency
• a clear quality model
• improvement processes
• automation
• Also,
• understanding user scenarios
Parsing in a broad sense
grouped tokens
typed tokens
slices/ tokens
raw string
visual diagram
graph model
vector drawing
raster picture
abstract model
concrete model
parse graph
parse forest
Zaytsev, Bagge, Parsing in a Broad Sense, MoDELS'
Introduction Compilers Grammarware
Transformation ConsistencyMaturity
Understanding Testing Conclusion
So, grammarware is based on grammars…
…can we test/validate it based on grammars?
Grammar-based testing• Purdom’s generator
• builds the shortest conforming term
• Maurer’s generator
• randomly selects alternatives
• Coverage criteria
• TC, NC, PC, BC, UC, CDBC
• Negative cases?
Fischer, Lämmel, Zaytsev, Comparison of CFGs Based on … Test Data
G G’
P P’
Combinatorial explosion
0
250
500
750
1,000
1,250
1,500
TC PC NC BC CDBC TC PC NC BC CDBC TC PC NC BC CDBC TC PC NC BC CDBC TC PC NC BC CDBC
Chart 2
Java (Habelitz) Java (Parr) Java (Stahl) Java (Studman) TESCOL (00001)
Fischer, Lämmel, Zaytsev, Comparison of CFGs Based on … Test Data
Combinatorial explosion
Figure 5.7: Number of generated sentences for UC
were not part of an optional or star expression. The number of generated sentences is mostly higherthan unfolding criteria with few equalities as shown in figure 5.9.
Figure 5.8: PC ratio for CDBC Figure 5.9: Number of sentences for CDBC
Figure 5.10 summarizes the number of sentences generated per coverage criteria for each grammarsample.
25
Butrus, Zaytsev, Grammar-based Testing Made Easy with Mutations
Nonterminal matching1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Fischer, Lämmel, Zaytsev, Comparison of CFGs Based on … Test Data
Badly matched:1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Fischer, Lämmel, Zaytsev, Comparison of CFGs Based on … Test Data
Differential methods
• Oracles are unnecessary
• Comparing grammars
• of varying structure, style, etc
• across TSs
• Investigate disagreements
G G’
P P’
McKeeman, Differential Testing for SoftwareSpinellis. Differential Debugging. IEEE Software, 2013
What is a bug?
The First Computer BugPhoto #
• Grammarware
processes
languages
• A bug is a program
• Inspect programs to
deal with bugs
Reality vs. specification
• Obtain a grammar
• Construct as an oracle
• Extract from the tool
• Infer from the codebase
• Converge/diff.test
Stevenson, Cordy, A Survey of Grammatical Inference in Software EngineeringRoș
Chapter 3. Deterministic Finite Automata 27
Accepted Node
Rejected NodeUnknown Node
(a) APTA Legend
0 1
0 1
0 1
0
0 1
R
1 6
2
3 4
5 7 8
9
(b) APTA state 0 (before any merge)
0 1
01 0 1
0
0
11
2 6
R
4 7 8
9
(c) APTA state i
0
10
1 0
1 0
0
1
1
2
6
8
R
(d) APTA state n
Figure 3.2: APTA states for Training Set 1 - EDSM (generated from C++)
Sample Training Set (clock_t) APTA (clock_t) EDSM (clock_t)Training Set 3.6.1 1 99 633Training Set 3.6.2 0 54 372Training Set 3.6.3 0 58 372Training Set 3.6.4 55 1687 239776Training Set 3.6.5 49 1886 323615Training Set 3.6.6 37 2400 x*Note 1: clock_t = clock ticks (processor time consumed by the program)
Note 2: * not successful (>10 minutes)
Table 3.2: Benchmarking results EDSM
Insights from replication
While benchmarking EDSM, we noticed that in its original form, the algorithm wascomputing merge scores for all possible red - blue combinations before every search.Because of that, it was not practical for larger training sets (e.g. the trainingsets from files). In order to mitigate this problem, we augmented the algorithmto calculate all merge scores only one time, and after each merge or promotion,compute the merge scores between the nodes e�ected by that action. In this way,the execution time decreased and we were able to test with first two training setsfrom file (3.6.4 and 3.6.5). The modification changed the merge order but the endresult is equivalent with the original.
Antipatterns
• Some ways lead to bugs faster
• Detect them => predict defects
• Smells
• left/right recursion
• ambiguous x*?
Taba, Khomh, Zou, Hassan, Nagappan, Predicting Bugs Using Antipatterns, ICSM 2013 Sajnani, Saini, Lopes, A Comparative Study of Bug Patterns in Java, SCAM 2014 Trubiani, Di Marco, Cortellessa, Mani, Petriu, Exploring Synergies…
Process improvement
• find defects
• fix defects
• learn how to fix defects
• learn to tolerate defects
• learn to avoid
Semiparsing• ad hoc lexical
analysis
• hierarchical lexical analysis
• lexical conceptual structure
• iterative lexical analysis
• fuzzy parsing
• parsing incomplete sentences
• island grammars
• lake grammars
• robust multilingual parsing
• gap parsing
• noise skipping
• bridge grammars
• skeleton grammars
• breadth-first parsing
• iterative syntactic analysis
• grammar relaxation
• agile parsing
• permissive grammars
• hierarchical error repair
• panic mode
• noncorrecting error recovery
• practical precise parsing
Zaytsev, Formal Foundations for Semi-parsing, CSMR-WCRE’
Conclusion
Grammarware is more than just compilers
Borrow methods from other domains
Automate whenever possible
Compare & combine
Advance taxonomies & formalisms
Bet on robust/tolerant methods
Thank you!
• Sources: • Figures used from own papers & talks
• + Eelco Visser’s keynote @ MODULARITY • + Tobias Baanders• + JLS book covers (Fair Use)
• Self-made screenshots • All photos from public domain • Comfortaa: font
Questions?
Introduction Compilers Grammarware
Transformation ConsistencyMaturity
Understanding Testing Conclusion
