Lgwa Loek Cleophas

Taxonomy-based software construction of SPARE Time: a case study

The authors consider the design and implementation of SPARE Time as an example of taxonomy-based software construction (TABASCO). SPARE Time is a C++ toolkit for keyword pattern matching. It differs from other toolkits in that it is based on a taxonomy of keyword pattern matching algorithms. They discuss the aims of TABASCO and of taxonomy construction and taxonomy-based toolkit design in particular. They show how the taxonomy structure induces the architecture and design of SPARE Time. The taxonomy-oriented derivation also makes the toolkit easier to understand and use. The toolkit – like the taxonomy – contains the well known Knuth–Morris–Pratt, Boyer–Moore, Aho–Corasick and Commentz-Walter algorithms, as well as variants thereof, including more recent algorithms such as Backward Oracle Matching. It is freely available for noncommercial use.

SPLicing TABASCO: Custom-Tailored Software Product Line Variants from Taxonomy-Based Toolkits

Taxonomy-Based Software Construction (TABASCO) applies extensive domain analyses to create conceptual hierarchies of algorithmic domains. Those are used as basis for the implementation of software toolkits. The monolithic structure of TABASCO-based toolkits restricts their adoption on resource-constrained or special-purpose devices. In this paper, we address this problem by applying Software Product Line (SPL) techniques to TABASCO-based toolkits: We use software taxonomies as input to creating a conceptual representation of variability as feature models of an SPL. We apply the variability realization mechanism delta modeling to transform realization artifacts, such as source code, to only contain elements for a particular selection of features. Our method is suitable for proactive, reactive and extractive SPL development so that it supports a seamless adoption and evolution of an SPL approach for TABASCO-based toolkits. We demonstrate the feasibility of the method with three case studies by proactively, reactively and extractively transforming TABASCO-based toolkits to SPLs, which allow derivation of variants with custom-tailored functionality.

Efficient automata constructions and approximate automata

In this paper, we present data structures and algorithms for efficiently constructing approximate automata. An approximate automaton for a regular language L is one which accepts at leastL. Such automata can be used in a variety of practical applications, including network security pattern matching, in which false-matches are only a performance nuisance. The construction algorithm is particularly efficient, and is tunable to yield more or less exact automata.

Using n-grams for the Automated Clustering of Structural Models

Model comparison and clustering are important for dealing with many models in data analysis and exploration, e.g. in domain model recovery or model repository management. Particularly in structural models, information is captured not only in model elements (e.g. in names and types) but also in the structural context, i.e. the relation of one element to the others. Some approaches involve a large number of models ignoring the structural context of model elements; others handle very few (typically two) models applying sophisticated structural techniques. In this paper we address both aspects and extend our previous work on model clustering based on vector space model, with a technique for incorporating structural context in the form of n-grams. We compare the n-gram accuracy on two datasets of Ecore metamodels in AtlanMod Zoo: small random samples using up to trigrams and a larger one (\({\sim }\)100 models) up to bigrams.

Correctness-by-Construction and Post-hoc Verification: A Marriage of Convenience?

Correctness-by-construction (CbC), traditionally based on weakest precondition semantics, and post-hoc verification (PhV) aspire to ensure functional correctness. We argue for a lightweight approach to CbC where lack of formal rigour increases productivity. In order to mitigate the risk of accidentally introducing errors during program construction, we propose to complement lightweight CbC with PhV. We introduce lightweight CbC by example and discuss strength and weaknesses of CbC and PhV and their combination, both conceptually and using a case study.

Correctness-by-Construction \wedge Taxonomies \Rightarrow Deep Comprehension of Algorithm Families

Correctness-by-construction (CbC) is an approach for developing algorithms inline with rigorous correctness arguments. A high-level specification is evolved into an implementation in a sequence of small, tractable refinement steps guaranteeing the resulting implementation to be correct. CbC facilitates the design of algorithms that are more efficient and more elegant than code that is hacked into correctness. In this paper, we discuss another benefit of CbC, i.e., that it supports the deep comprehension of algorithm families. We organise the different refinements of the algorithms carried out during CbC-based design in a taxonomy. The constructed taxonomy provides a classification of the commonality and variability of the algorithm family and, hence, provides deep insights into their structural relationships. Such taxonomies together with the implementation of the algorithms as toolkits provide an excellent starting point for extractive and proactive software product line engineering.

N-Gram Representations For Comment Filtering

Accurate classifiers for short texts are valuable assets in many applications. Especially in online communities, where users contribute to content in the form of posts and comments, an effective way of automatically categorising posts proves highly valuable. This paper investigates the use of N-grams as features for short text classification, and compares it to manual feature design techniques that have been popular in this domain. We find that the N-gram representations greatly outperform manual feature extraction techniques.

Backward linearised tree pattern matching

We present a new backward tree pattern matching algorithm for ordered trees. The algorithm finds all occurrences of a single given tree pattern which match an input tree. It makes use of linearisations of both the given pattern and the input tree. The algorithm preserves the properties and advantages of standard backward string pattern matching approaches. The number of symbol comparisons in the backward tree pattern matching can be sublinear in the size of the input tree. As in the case of backward string pattern matching, the size of the bad character shift table used by the algorithm is linear in the size of the alphabet. We compare the new algorithm with best performing previously existing algorithms based on (non-linearised) tree pattern matching using finite tree automata or stringpath matchers and show that it outperforms these for single pattern matching.

Detecting Variability in MATLAB/Simulink Models: An Industry-Inspired Technique and its Evaluation

Model-based languages such as MATLAB/Simulink play an essential role in the model-driven development of software systems. To comply with new requirements, it is common practice to create new variants by copying existing systems and modifying them. Commonly referred to as clone-and-own, severe problems arise in the long-run when no dedicated variability management is installed. To allow for a documented and structured reuse of systems, their variability information needs to be reverse-engineered. In this paper, we propose an advanced comparison procedure, the Matching Window Technique, and a customizable metric. Both allow us to overcome structural alterations commonly performed during clone-and-own. We analyze related MATLAB/Simulink models and determine, classify and represent their variability information in an understandable way. With our technique, we assist model engineers in maintaining and evolving existing variants. We provide three feasibility studies with real-world models from the automotive domain and show our technique to be fast and precise. Furthermore, we perform semi-structured interviews with domain experts to assess the potential applicability of our technique in practice.

Clustering variation points in MATLAB/Simulink models using reverse signal propagation analysis

Model-based languages such as MATLAB/Simulink play an essential role in the model-driven development of software systems. During their development, these systems can be subject to modification numerous times. For large-scale systems, to manually identify performed modifications is infeasible. However, their precise identification and subsequent validation is essential for the evolution of model-based systems. If not fully identified, modifications may cause unaccountable behavior as the system evolves and their redress can significantly delay the entire development process. In this paper, we propose a fully automated technique called Reverse Signal Propagation Analysis, which identifies and clusters variations within evolving MATLAB/Simulink models. With each cluster representing a clearly delimitable variation point between models, we allow model engineers not only to specifically focus on single variations, but by using their domain knowledge, to also relate and verify them. By identifying variation points, we assist model engineers in validating the respective parts and reduce the risk of improper system behavior as the system evolves. To assess the applicability of our technique, we present a feasibility study with real-world models from the automotive domain and show our technique to be very fast and highly precise.