Laurence Tratt

Detecting Ambiguity in Programming Language Grammars

Ambiguous Context Free Grammars (CFGs) are problematic for programming languages, as they allow inputs to be parsed in more than one way. In this paper, we introduce a simple non-deterministic search-based approach to ambiguity detection which non-exhaustively explores a grammar in breadth for ambiguity. We also introduce two new techniques for generating random grammars – Boltzmann sampling and grammar mutation – allowing us to test ambiguity detection tools on much larger corpuses than previously possible. Our experiments show that our breadth-based approach to ambiguity detection performs as well as, and generally better, than extant tools.

Model transformations and tool integration

Model transformations are increasingly recognised as being of significant importance to many areas of software development and integration. Recent attention on model transformations has particularly focused on the OMG’s Queries/Views/Transformations (QVT) Request for Proposals (RFP). In this paper I motivate the need for dedicated approaches to model transformations, particularly for the data involved in tool integration, outline the challenges involved, and then present a number of technologies and techniques which allow the construction of flexible, powerful and practical model transformations.

Experimental assessment of software metrics using automated refactoring

A large number of software metrics have been proposed in the literature, but there is little understanding of how these metrics relate to one another. We propose a novel experimental technique, based on search-based refactoring, to assess software metrics and to explore relationships between them. Our goal is not to improve the program being refactored, but to assess the software metrics that guide the automated refactoring through repeated refactoring experiments. We apply our approach to five popular cohesion metrics using eight real-world Java systems, involving 300,000 lines of code and over 3,000 refactorings. Our results demonstrate that cohesion metrics disagree with each other in 55% of cases, and show how our approach can be used to reveal novel and surprising insights into the software metrics under investigation.

Domain specific language implementation via compile-time meta-programming

Domain specific languages (DSLs) are mini-languages that are increasingly seen as being a valuable tool for software developers and non-developers alike. DSLs must currently be created in an ad-hoc fashion, often leading to high development costs and implementations of variable quality. In this article, I show how expressive DSLs can be hygienically embedded in the Converge programming language using its compile-time meta-programming facility, the concept of DSL blocks, and specialised error reporting techniques. By making use of pre-existing facilities, and following a simple methodology, DSL implementation costs can be significantly reduced whilst leading to higher quality DSL implementations.

Model transformations in practice workshop

Model Transformations in Practice (MTiP) 2005 was a workshop which provided a forum for the model transformation community to discuss practical model transformation issues. Although many different model transformation approaches have been proposed and explored in recent years, there has been little work on comparing and contrasting various approaches. Without such comparisons, it is hard to assess new model transformation approaches such as the upcoming OMG MOF/QVT recommendation, or to discern sensible future paths for the area. Our aims with the workshop were to create a forum that would help lead to an increased understanding of the relative merits of different model transformation techniques and approaches. A more advanced understanding of such merits is of considerable benefit to both the model transformation and wider modelling communities.

Evaluating and comparing language workbenches

Language workbenches are environments for simplifying the creation and use of computer languages. The annual Language Workbench Challenge (LWC) was launched in 2011 to allow the many academic and industrial researchers in this area an opportunity to quantitatively and qualitatively compare their approaches. We first describe all four LWCs to date, before focussing on the approaches used, and results generated, during the third LWC. We give various empirical data for ten approaches from the third LWC. We present a generic feature model within which the approaches can be understood and contrasted. Finally, based on our experiences of the existing LWCs, we propose a number of benchmark problems for future LWCs. HighlightsWe describe the challenges posed by the 4 Language Workbench Challenges run so far.We establish a feature model that captures the design space of language workbenches.We classify 10 language workbenches according to our feature model.We present empirical data on 10 implementations of a questionnaire DSL.We propose benchmark problems to be used in future Language Workbench Challenges.

Dynamically Typed Languages

Abstract Dynamically typed languages such as Python and Ruby have experienced a rapid grown in popularity in recent times. However, there is much confusion as to what makes these languages interesting relative to statically typed languages, and little knowledge of their rich history. In this chapter, I explore the general topic of dynamically typed languages, how they differ from statically typed languages, their history, and their defining features.

Control Dependence for Extended Finite State Machines

Though there has been nearly three decades of work on program slicing, there has been comparatively little work on slicing for state machines. One of the primary challenges that currently presents a barrier to wider application of state machine slicing is the problem of determining control dependence. We survey existing related definitions, introducing a new definition that subsumes one and extends another. We illustrate that by using this new definition our slices respect Weiser slicings termination behaviour. We prove results that clarify the relationships between our definition and older ones, following this up with examples to motivate the need for these differences.

Storage strategies for collections in dynamically typed languages

Dynamically typed language implementations often use more memory and execute slower than their statically typed cousins, in part because operations on collections of elements are unoptimised. This paper describes storage strategies, which dynamically optimise collections whose elements are instances of the same primitive type. We implement storage strategies in the PyPy virtual machine, giving a performance increase of 18% on wide-ranging benchmarks of real Python programs. We show that storage strategies are simple to implement, needing only 1500LoC in PyPy, and have applicability to a wide range of virtual machines.

A theoretical and empirical study of EFSM dependence

Dependence analysis underpins many activities in software maintenance such as comprehension and impact analysis. As a result, dependence has been studied widely for programming languages, notably through work on program slicing. However, there is comparatively little work on dependence analysis at the model level and hitherto, no empirical studies. We introduce a slicing tool for Extended Finite State Machines (EFSMs) and use the tool to gather empirical results on several forms of dependence found in ten EFSMs, including well-known benchmarks in addition to real-world EFSM models. We investigate the statistical properties of dependence using statistical tests for correlation and formalize and prove four of the empirical findings arising from our empirical study. The paper thus provides the maintainer with both empirical data and foundational theoretical results concerning dependence in EFSM models.