Context: There are numerous textual notations and techniques that can be used in requirements engineering. Currently, practitioners make a choice without having scientific evidence regarding their suitability for given tasks. This uninformed choice may affect task performance. Objective: In this research, we investigate the adequacy of two well-known notations: use cases and user stories, as a starting point for the manual derivation of a structural conceptual model that represents the domain of the system. We also examine other factors that may affect the performance of this task. Methods: This work relies on two experiments. The first is a controlled classroom experiment. The second one is a quasi-experiment, conducted over multiple weeks, that aims at evaluating the quality of the derived conceptual model in light of the notation used, the adopted derivation process, and the complexity of the system to be. We measure quality in terms of validity and completeness of the conceptual model. Results: The results of the controlled experiment indicate that, for deriving conceptual models, user stories fit better than use cases. Yet, the second experiment indicates that the quality of the derived conceptual models is affected mainly by the derivation process and by the complexity of the case rather than the notation used. Contribution: We present evidence that the task of deriving a conceptual model is affected significantly by additional factors other than requirements notations. Furthermore, we propose implications and hypotheses that pave the way for further studies that compare alternative notations for the same task as well as for other tasks. Practitioners may use our findings to analyze the factors that affect the quality of the conceptual model when choosing a requirements notation and an elicitation technique that best fit their needs.

On deriving conceptual models from user requirements: An empirical study

User stories are primary requirements artifacts within agile methods. They are comprised of short sentences written in natural language expressing units of functionality for the to-be system. Despite their simple format, when modelers are faced with a set of user stories they might be having difficulty in sorting them, evaluating their redundancy, and assessing their relevancy in the effort to prioritize them. The present paper tests the ability of modelers to understand the requirements problem through a visual representation (named the Rationale Tree) which is a conceptual model and is built out of a user stories’ set. The paper is built upon and extends previous work relating to the feasibility of generating such a representation out of a user stories’ set by comparing the performance of the Rationale Tree with the User Story Mapping approach. This is achieved by performing a two-group quantitative comparative study. The identified comparative variables for each method were understandability, recognition of missing requirements/epics/themes, and adaptability. The Rational Tree was not easy to understand and did not perform as anticipated in assisting with the recognition of missing requirements/epics/themes. However, its employment allowed modelers to offer qualitative representations of a specific software problem. Overall, the present experiment evaluates whether a conceptual model could be a consistent solution towards the holistic comprehension of a software development problem within an agile setting, compared to more ‘conventional’ techniques used so far.

Conceptual Modeling Versus User Story Mapping: Which is the Best Approach to Agile Requirements Engineering?

Validation of software requirements is a primary phase in requirements engineering that ensures requirements match the target system with the intended needs of the acquirer. It aims to detect and correct errors that prevail in the specified requirements. Although there are tremendous requirements validation approaches, some software may fail because of limited or ineffective requirements validation techniques and unreliable requirements’ quality characteristics. In this study, a systematic literature review of requirements validation is performed. The study analyzes the most adopted validation techniques, reports requirements quality characteristics, and discovers significant challenges of validation techniques. The review identified 66 relevant primary studies analyzed to derive deep insights into the following aspects of requirements validation: trends of requirements validation methods, including their subtechnique strengths and weaknesses, requirements quality characteristics categories, and adopted tools and datasets in these techniques. We grouped validation techniques into categories: prototyping, inspection, knowledge-oriented, test-oriented, modeling and assessment, and formal models. The analysis reported 19 validation techniques, 27 tools, new requirements validation characteristics, and several challenges that prevailed through validation techniques. The trend of validation techniques is to those methods that apply machine learning techniques with knowledge from dictionaries and ontologies. Most challenges are about how to express the requirements and how to revert clients’ feedback. There is a strong relationship between validation techniques, software application domain, and requirements validation quality attributes. Thus, there is an immense need to unify the quality characteristics and domain-specific validation methods.

/pdf/challenges-of-software-requirements-quality-assurance-and-cv1c96n1go.pdf

Challenges of Software Requirements Quality Assurance and Validation: A Systematic Literature Review

Abstract[Context and Motivation] User stories are a popular notation for representing requirements, especially in agile development. Although they represent a cornerstone for developing systems, limited research exists on how user stories are refined into conceptual design. [Question/Problem] We study the process of deriving conceptual models from user stories, which is at the basis of information systems development. We focus our attention on the derivation of a holistic view of structural and interaction aspects, represented via class diagrams and use case diagrams, respectively. In this paper, we examine whether providing guidelines has an effect on the ability of humans to derive complete and valid conceptual models. [Principal Ideas/Results] We design example-based guidelines for the derivation of class and use case diagrams from user stories. Then, we conduct a two-factor, two-treatment controlled experiment with 77 undergraduate students serving as subjects. The results indicate that the guidelines improve the completeness and validity of the conceptual models in cases of medium complexity, although the subjects were neutral on the usefulness of the guidelines. [Contribution] The guidelines may assist analysts in the refinement of user stories. Our initial evidence, besides showing how the guidelines can help junior analysts derive high-quality conceptual models, opens the doors for further studies on the refinement of user stories, and to the investigation of alternative guidelines.KeywordsRequirements engineeringConceptual modelingUse casesDerivation processGuidelinesClass diagramUser storiesControlled experiment 

Guided Derivation of Conceptual Models from User Stories: A Controlled Experiment

[Context and motivation] Conversations around requirements, such as interviews and workshops, are a key activity of requirements elicitation, and play a significant role in the creation of requirements specifications. [Question/problem] While these conversations contain a wealth of knowledge, requirements engineers use them mainly through note-taking during the conversation and by recalling the information from their memory. There is potential for supporting practitioners by retrieving important information from the recordings of these conversations. [Principal ideas/results] Although transcriptions can be automatically generated with good accuracy, they often contain excessive text to be efficiently used for processing requirements elicitation sessions. Thus, we observed a need to transform these datasets into a useful format for requirements engineers to analyze. [Contribution] We present REConSum, a prototype that utilizes Natural Language Processing (NLP) to summarize requirements conversations. REConSum takes as input a transcribed conversation, and it filters the speaker turns by keeping only those that include a question and that are expected to contain, or to be answered with, requirements-relevant information. In addition to presenting REConSum, we experiment with different algorithms to assess the most effective combination. 

Patrizia Gieske

Papers

On deriving conceptual models from user requirements: An empirical study

Extraction of Conceptual Models: User Stories vs. Use Cases