Parsing Expression Grammars Made Practical

Parsing Expression Grammars (PEGs) define languages by specifying recursive-descent parser that recognises them. The PEG formalism exhibits desirable properties, such as closure under composition, built-in disambiguation, unification of syntactic and lexical concerns, and closely matching programmer intuition. Unfortunately, state of the art PEG parsers struggle with left-recursive grammar rules, which are not supported by the original definition of the formalism and can lead to infinite recursion under naive implementations. Likewise, support for associativity and explicit precedence is spotty. To remedy these issues, we introduce Autumn, a general purpose PEG library that supports left-recursion, left and right associativity and precedence rules, and does so efficiently. Furthermore, we identify infix and postfix operators as a major source of inefficiency in left-recursive PEG parsers and show how to tackle this problem. We also explore the extensibility of the PEG paradigm by showing how one can easily introduce new parsing operators and how our parser accommodates custom memoization and error handling strategies. We compare our parser to both state of the art and battle-tested PEG and CFG parsers, such as Rats!, Parboiled and ANTLR.Comment: "Proceedings of the International Conference on Software Language Engineering (SLE 2015)" - 167-172 (ISBN : 978-1-4503-3686-4

Solving the TTC 2011 Reengineering Case with GrGen.NET

The challenge of the Reengineering Case is to extract a state machine model out of the abstract syntax graph of a Java program. The extracted state machine offers a reduced view on the full program graph and thus helps to understand the program regarding the question of interest. We tackle this task employing the general purpose graph rewrite system GrGen.NET (www.grgen.net).Comment: In Proceedings TTC 2011, arXiv:1111.440

Automatic Generation of Text Descriptive Comments for Code Blocks

We propose a framework to automatically generate descriptive comments for source code blocks. While this problem has been studied by many researchers previously, their methods are mostly based on fixed template and achieves poor results. Our framework does not rely on any template, but makes use of a new recursive neural network called Code-RNN to extract features from the source code and embed them into one vector. When this vector representation is input to a new recurrent neural network (Code-GRU), the overall framework generates text descriptions of the code with accuracy (Rouge-2 value) significantly higher than other learning-based approaches such as sequence-to-sequence model. The Code-RNN model can also be used in other scenario where the representation of code is required.Comment: aaai 201

Efficient Monitoring of Parametric Context Free Patterns

Recent developments in runtime verification and monitoring show that parametric regular and temporal logic specifications can be efficiently monitored against large programs. However, these logics reduce to ordinary finite automata, limiting their expressivity. For example, neither can specify structured properties that refer to the call stack of the program. While context-free grammars (CFGs) are expressive and well-understood, existing techniques of monitoring CFGs generate massive runtime overhead in real-life applications. This paper shows for the first time that monitoring parametric CFGs is practical (on the order of 10% or lower for average cases, several times faster than the state-of-the-art). We present a monitor synthesis algorithm for CFGs based on an LR(1) parsing algorithm, modified with stack cloning to account for good prefix matching. In addition, a logic-independent mechanism is introduced to support partial matching, allowing patterns to be checked against fragments of execution traces

Panel discussion: Proposals for improving OCL

During the panel session at the OCL workshop, the OCL community discussed, stimulated by short presentations by OCL experts, potential future extensions and improvements of the OCL. As such, this panel discussion continued the discussion that started at the OCL meeting in Aachen in 2013 and on which we reported in the proceedings of the last year's OCL workshop. This collaborative paper, to which each OCL expert contributed one section, summarises the panel discussion as well as describes the suggestions for further improvements in more detail.Peer ReviewedPostprint (published version

A Type System for Tom

Extending a given language with new dedicated features is a general and quite used approach to make the programming language more adapted to problems. Being closer to the application, this leads to less programming flaws and easier maintenance. But of course one would still like to perform program analysis on these kinds of extended languages, in particular type checking and inference. In this case one has to make the typing of the extended features compatible with the ones in the starting language. The Tom programming language is a typical example of such a situation as it consists of an extension of Java that adds pattern matching, more particularly associative pattern matching, and reduction strategies. This paper presents a type system with subtyping for Tom, that is compatible with Java's type system, and that performs both type checking and type inference. We propose an algorithm that checks if all patterns of a Tom program are well-typed. In addition, we propose an algorithm based on equality and subtyping constraints that infers types of variables occurring in a pattern. Both algorithms are exemplified and the proposed type system is showed to be sound and complete