DREQUS: an approach for the Discovery of REQuirements Using Scenarios

ABSTRACT: Requirements engineering is recognized as a complex cognitive problem-solving process that takes place in an unstructured and poorly-understood problem context. Requirements elicitation is the activity generally regarded as the most crucial step in the requirements engineering process. The term “elicitation” is preferred to “capture”, to avoid the suggestion that requirements are out there to be collected. Information gathered during requirements elicitation often has to be interpreted, analyzed, modeled, and validated before the requirements engineer can feel confident that a complete set of requirements of a system have been obtained. Requirements elicitation comprises the set of activities that enable discovering, understanding, and documenting the goals and motives for building a proposed software system. It also involves identifying the requirements that the resulting system must satisfy in to achieve these goals. The requirements to be elicited may range from modifications to well-understood problems and systems (i.e. software upgrades), to hazy understandings of new problems being automated, to relatively unconstrained requirements that are open to innovation (e.g. mass-market software). Requirements elicitation remains problematic; missing or mistaken requirements still delay projects and cause cost overruns. No firm definition has matured for requirements elicitation in comparison to other areas of requirements engineering. This research is aimed to improve the results of the requirements elicitation process directly impacting the quality of the software products derived from them

Maps of Lessons Learnt in Requirements Engineering

Both researchers and practitioners have emphasized the importance of learning from past experiences and its consequential impact on project time, cost, and quality. However, from the survey we conducted of requirements engineering (RE) practitioners, over 70\% of the respondents stated that they seldom use RE lessons in the RE process, though 85\% of these would use such lessons if readily available. Our observation, however, is that RE lessons are scattered, mainly implicitly, in the literature and practice, which obviously, does not help the situation. We, therefore, present ``maps” of RE lessons which would highlight weak (dark) and strong (bright) areas of RE (and hence RE theories). Such maps would thus be: (a) a driver for research to ``light up” the darker areas of RE and (b) a guide for practice to benefit from the brighter areas. To achieve this goal, we populated the maps with over 200 RE lessons elicited from literature and practice using a systematic literature review and survey. The results show that approximately 80\% of the elicited lessons are implicit and that approximately 70\% of the lessons deal with the elicitation, analysis, and specification RE phases only. The RE Lesson Maps, elicited lessons, and the results from populating the maps provide novel scientific groundings for lessons learnt in RE as this topic has not yet been systematically studied in the field

Digital Government Systems: Tackling The Legacy Problem Through A Game-Based Approach To Business Requirements Analysis

Government agency reliance on legacy systems is problematic: they are costly to maintain, difficult to integrate with and they hinder innovation. However, the replacement of legacy systems is not a straightforward endeavor, and it often results in technology substitution that is not accompanied by business process change. The focus of this dissertation is on the phenomenon of legacy system replication wherein the requirements for applications replacing outdated technologies mimic legacy features and reflect status quo operational processes that have been historically shaped by the legacy system itself. This problem is referred to throughout the dissertation as the “legacy problem.” The dissertation investigates its roots and proposes an approach to overcome it. Specifically, a mixed method research approach is taken, including a survey of public sector practitioners to explore the extent of the legacy problem, and a series of semi-structured interviews with government information technology and management professionals to delve into the dynamics of legacy system replacement projects. Findings indicate that the legacy problem often stems from a lack of critical analysis of business requirements and the desire to minimize the risks associated with organizational change, which often result in missed opportunities for digital government innovation. As a consequence, the dissertation proposes a candidate approach to deal with the legacy problem in the development of a requirements game (RE-PROVO) which supports requirements discussions structured around the themes of legacy (or heritage) preservation and innovation. The game is evaluated by local government practitioners through several iterations and their feedback is analysed to gauge the potential utility of the approach. The results indicate that with a streamlined user interface and accentuated game elements RE-PROVO can be a valuable and effective tool for requirements analysis in legacy system replacement projects

EFFECTIVE METHODS AND TOOLS FOR MINING APP STORE REVIEWS

Research on mining user reviews in mobile application (app) stores has noticeably advanced in the past few years. The main objective is to extract useful information that app developers can use to build more sustainable apps. In general, existing research on app store mining can be classified into three genres: classification of user feedback into different types of software maintenance requests (e.g., bug reports and feature requests), building practical tools that are readily available for developers to use, and proposing visions for enhanced mobile app stores that integrate multiple sources of user feedback to ensure app survivability. Despite these major advances, existing tools and techniques still suffer from several drawbacks. Specifically, the majority of techniques rely on the textual content of user reviews for classification. However, due to the inherently diverse and unstructured nature of user-generated online textual reviews, text-based review mining techniques often produce excessively complicated models that are prone to over-fitting. Furthermore, the majority of proposed techniques focus on extracting and classifying the functional requirements in mobile app reviews, providing a little or no support for extracting and synthesizing the non-functional requirements (NFRs) raised in user feedback (e.g., security, reliability, and usability). In terms of tool support, existing tools are still far from being adequate for practical applications. In general, there is a lack of off-the-shelf tools that can be used by researchers and practitioners to accurately mine user reviews. Motivated by these observations, in this dissertation, we explore several research directions aimed at addressing the current issues and shortcomings in app store review mining research. In particular, we introduce a novel semantically aware approach for mining and classifying functional requirements from app store reviews. This approach reduces the dimensionality of the data and enhances the predictive capabilities of the classifier. We then present a two-phase study aimed at automatically capturing the NFRs in user reviews. We also introduce MARC, a tool that enables developers to extract, classify, and summarize user reviews