Nathan Weston

A Formal Approach to Semantic Composition of Aspect-Oriented Requirements

The goal of aspect-oriented requirements engineering (AORE) is to identify possible crosscutting concerns, and to develop composition specifications around those concerns. These compositions can be used to reason about potential conflicts in the requirements and to relate requirements to architecture in semantically meaningful ways. Recent work in AORE has moved from a syntactic approach to composition, which leads to fragile compositions and increased coupling between aspect and base concerns, to a semantic composition approach, based on semantics of the natural language itself. However, such compositions are at present only informally specified, and as such formal reasoning about the requirements and the subsequent derivations are difficult. We present a formal approach to these semantic-based compositions which facilitates this reasoning. We show that the approach especially lends itself to identifying conflicts between requirements and mapping compositions to a derived architecture.

EA-Analyzer: automating conflict detection in a large set of textual aspect-oriented requirements

One of the aims of Aspect-Oriented Requirements Engineering is to address the composability and subsequent analysis of crosscutting and non-crosscutting concerns during requirements engineering. A composition definition explicitly represents interdependencies and interactions between concerns. Subsequent analysis of such compositions helps to reveal conflicting dependencies that need to be resolved in requirements. However, detecting conflicts in a large set of textual aspect-oriented requirements is a difficult task as a large number of explicitly defined interdependencies need to be analyzed. This paper presents EA-Analyzer, the first automated tool for identifying conflicts in aspect-oriented requirements specified in natural-language text. The tool is based on a novel application of a Bayesian learning method. We present an empirical evaluation of the tool with three industrial-strength requirements documents from different domains and a fourth academic case study used as a de facto benchmark in several areas of the aspect-oriented community. This evaluation shows that the tool achieves up to 93.90 % accuracy regardless of the documents chosen as the training and validation sets.

Formal semantic conflict detection in aspect-oriented requirements

The goal of Aspect-Oriented Requirements Engineering is to identify possible crosscutting concerns, and to develop composition specifications, which can be used to reason about potential conflicts in the requirements. Recent work in AORE has moved from a syntactic approach to composition, which leads to fragile compositions and increased coupling between aspect and base concerns, to a semantic composition approach, based on semantics of the natural language itself. However, such compositions are at present only informally specified, and as such precise conflict detection between the requirements compositions is difficult. We present an approach for the formalisation of these semantic-based compositions which means that logical conflicts between compositions can be precisely identified and understood semantically. We show that the approach can be supported by off-the-shelf tools, meaning it is scalable and feasible for even large requirements specifications.

EA-Analyzer: Automating Conflict Detection in Aspect-Oriented Requirements

One of the aims of Aspect-Oriented Requirements Engineering is to address the composability and subsequent analysis of crosscutting and non-crosscutting concerns during requirements engineering. Composing concerns may help to reveal conflicting dependencies that need to be identified and resolved. However, detecting conflicts in a large set of textual aspect-oriented requirements is an error-prone and time-consuming task. This paper presents EA-Analyzer, the first automated tool for identifying conflicts in aspect-oriented requirements specified in natural-language text. The tool is based on a novel application of a Bayesian learning method that has been effective at classifying text. We present an empirical evaluation of the tool with three industrial-strength requirements documents from different real-life domains. We show that the tool achieves up to 92.97% accuracy when one of the case study documents is used as a training set and the other two as a validation set.

Aspect Identification in Textual Requirements with EA-Miner

This chapter presents a methodology for identification of crosscutting concerns in textual requirements along with its supporting tool EA-Miner. This chapter discusses how EA-Miner uses natural language processing techniques in aspect identification and structuring using a requirements level feature model as an example. The process is illustrated using the Car Crash case study.

ArborCraft: automatic feature models from textual requirements documents

One of the tasks facing requirements engineers working in the field of Product Line Engineering is the creation of feature models, which represent the domain of the product line and from which product configurations can be derived. Requirements documents, which are to be mined for this information, are often very large and written in potentially ambiguous natural language, and can be written over a long period of time by various authors. This makes the engineers task very arduous, and a clear separation of concerns is often difficult to infer from textual documents. We present ArborCraft, a tool which automatically mines textual requirements documents, identifies features based on natural-language processing techniques, and produces a candidate feature model. We show that this can significantly reduce the burden on the requirements engineer and promote separation of concerns early in the Product Line Engineering process.

EA-Analyzer: a tool for identifying conflicting dependencies in requirements documents

Aspect-oriented requirements engineering is a promising approach to early aspects, and its main goal is to address the composability and subsequent analysis of crosscutting concerns during requirements engineering. The goal is to reveal aspect influences and mutual tradeoffs among aspects before the architecture is derived. An important step towards the effective analysis of early aspects is to provide tools for mining aspects in requirements document, and inferring dependencies and trade-offs among broadly-scoped concerns. However, to date, current techniques for the analysis of early aspects have mainly been limited to techniques for mining aspects using natural language processing or information retrieval. The analysis of inferences and trade-offs are still being done manually with visual inspection. EA-Analyzer is a tool for indentifying conflicting dependencies that are contained within a requirements document. The tool is an application of a Bayesian learning method, and offers early insights into architectural trade-offs.