Research Findings on Empirical Evaluation of Requirements Specifications Approaches

Numerous software requirements specification (SRS) approaches have been proposed in software engineering. However, there has been little empirical evaluation of the use of these approaches in specific contexts. This paper describes the results of a mapping study, a key instrument of the evidence-based paradigm, in an effort to understand what aspects of SRS are evaluated, in which context, and by using which research method. On the basis of 46 identified and categorized primary studies, we found that understandability is the most commonly evaluated aspect of SRS, experiments are the most commonly used research method, and the academic environment is where most empirical evaluation takes place

A Study of Text Mining Framework for Automated Classification of Software Requirements in Enterprise Systems

abstract: Text Classification is a rapidly evolving area of Data Mining while Requirements Engineering is a less-explored area of Software Engineering which deals the process of defining, documenting and maintaining a software system's requirements. When researchers decided to blend these two streams in, there was research on automating the process of classification of software requirements statements into categories easily comprehensible for developers for faster development and delivery, which till now was mostly done manually by software engineers - indeed a tedious job. However, most of the research was focused on classification of Non-functional requirements pertaining to intangible features such as security, reliability, quality and so on. It is indeed a challenging task to automatically classify functional requirements, those pertaining to how the system will function, especially those belonging to different and large enterprise systems. This requires exploitation of text mining capabilities. This thesis aims to investigate results of text classification applied on functional software requirements by creating a framework in R and making use of algorithms and techniques like k-nearest neighbors, support vector machine, and many others like boosting, bagging, maximum entropy, neural networks and random forests in an ensemble approach. The study was conducted by collecting and visualizing relevant enterprise data manually classified previously and subsequently used for training the model. Key components for training included frequency of terms in the documents and the level of cleanliness of data. The model was applied on test data and validated for analysis, by studying and comparing parameters like precision, recall and accuracy.Dissertation/ThesisMasters Thesis Engineering 201

Using a Semantic Wiki for Documentation Management in Very Small Projects

International audienceThe emerging ISO/IEC 29110 standard Lifecycle profiles for Very Small Entities is targeted at very small entity (VSE) having up to 25 people, to assist them unlock the potential benefits of using software engineering standards. VSEs may use semantic web technologies to improve documentation management infrastructure and processes. We proposed to use a semantic wiki for documentation management based on an identification scheme inspired from an IFLA proposition called Functional Requirements for Bibliographic Records. The document identification scheme allows documents to be managed by the internal resource management of the semantic wiki, hence benefiting from a straightforward but powerful version control. With few inputs of semantic annotations by VSE employees - through usable semantic forms and templates, the semantic wiki acts as a library catalog, and users can find, identify, select, obtain, and navigate resources

Relationship analysis : improving the systems analysis process

A significant aspect of systems analysis involves discovering and representing entities and their inter-relationships. Guidelines exist to identify entities but do not provide a rigorous and comprehensive process to explicitly capture the relationship structure of the problem domain. Whereas, other analysis techniques lightly address the relationship discovery process, Relationship Analysis is the only systematic, domain-independent analysis technique focusing exclusively on a domain\u27s relationship structure. The quality of design artifacts, such as class diagrams, and development time necessary to generate these artifacts can be improved by first representing the complete relationship structure of the problem domain. The Relationship Analysis Model is the first theory-based taxonomy to classify relationships. A rigorous evaluation was conducted, including a formal experiment comparing novice and experienced analysts with and without Relationship Analysis. It was shown that the Relationship Analysis Process based on the model does provide a fuller and richer systems analysis, resulting in improved quality of and reduced time in generating class diagrams. It also was shown that Relationship Analysis enables analysts of varying experience levels to achieve a similar level of quality of class diagrams. Relationship Analysis significantly enhances the systems analyst\u27s effectiveness, especially in the area of relationship discovery and documentation resulting in improved analysis and design artifacts

Requirements Modeling Methodology Based on Knowledge Engineering: A Case Study of Railway Control System

The complexity of the verification and the validation of embedded systems is increasing. This paper explores the first requirements engineering processes in the solution domain, which are analysis and specification. In this work we present an architecture of a requirement specification system. We show how the requirements are analysed and structured to generate a dependency graph. This latter will serve to analyse requirements and to model specifications on goal model. In this paper we will focus on the analysis, and structuring processes. We will explain the requirement classification criteria. Keywords: Requirements Modeling, Qualification Strategy, Knowledge Engineering, Ontology, Dependency Graph, Embedded System, ERTMS/ETC

Automatic Transformation of Natural to Unified Modeling Language: A Systematic Review

Context: Processing Software Requirement Specifications (SRS) manually takes a much longer time for requirement analysts in software engineering. Researchers have been working on making an automatic approach to ease this task. Most of the existing approaches require some intervention from an analyst or are challenging to use. Some automatic and semi-automatic approaches were developed based on heuristic rules or machine learning algorithms. However, there are various constraints to the existing approaches of UML generation, such as restriction on ambiguity, length or structure, anaphora, incompleteness, atomicity of input text, requirements of domain ontology, etc. Objective: This study aims to better understand the effectiveness of existing systems and provide a conceptual framework with further improvement guidelines. Method: We performed a systematic literature review (SLR). We conducted our study selection into two phases and selected 70 papers. We conducted quantitative and qualitative analyses by manually extracting information, cross-checking, and validating our findings. Result: We described the existing approaches and revealed the issues observed in these works. We identified and clustered both the limitations and benefits of selected articles. Conclusion: This research upholds the necessity of a common dataset and evaluation framework to extend the research consistently. It also describes the significance of natural language processing obstacles researchers face. In addition, it creates a path forward for future research