Sandeep Reddivari

Visual requirements analytics: a framework and case study

For many software projects, keeping requirements on track needs an effective and efficient path from data to decision. Visual analytics creates such a path that enables the human to extract insights by interacting with the relevant information. While various requirements visualization techniques exist, few have produced end-to-end value to practitioners. In this paper, we advance the literature on visual requirements analytics by characterizing its key components and relationships in a framework. We follow the goal–question–metric paradigm to define the framework by teasing out five conceptual goals (user, data, model, visualization, and knowledge), their specific operationalizations, and their interconnections. The framework allows us to not only assess existing approaches, but also create tool enhancements in a principled manner. We evaluate our enhanced tool support through a case study where massive, heterogeneous, and dynamic requirements are processed, visualized, and analyzed. Working together with practitioners on a contemporary software project within its real-life context leads to the main finding that visual analytics can help tackle both open-ended visual exploration tasks and well-structured visual exploitation tasks in requirements engineering. In addition, the study helps the practitioners to reach actionable decisions in a wide range of areas relating to their project, ranging from theme and outlier identification, over requirements tracing, to risk assessment. Overall, our work illuminates how the data-to-decision analytical capabilities could be improved by the increased interactivity of requirements visualization.

ReCVisu: A tool for clustering-based visual exploration of requirements

Clustering is of great practical value in discovering natural groupings of large numbers of requirements artifacts. Clustering-based visualization has shown promise in supporting requirements tracing. In this paper, we transform the success to a wider range of clustering-based visual exploration tasks in requirements engineering. We describe ReCVisu, a requirements exploration tool based on quantitative visualizations. We discuss the key features of ReCVisu and its potential improvements over previous work.

Keeping requirements on track via visual analytics

For many software projects, keeping requirements on track needs an effective and efficient path from data to decision. Visual analytics creates such a path that enables the human to extract insights by interacting with the relevant information. While various requirements visualization techniques exist, few have produced end-to-end values to practitioners. In this paper, we advance the literature on visual requirements analytics by characterizing its key components and relationships. This allows us to not only assess existing approaches, but also create tool enhancements in a principled manner. We evaluate our enhanced tool supports through a case study where massive, heterogeneous, and dynamic requirements are processed, visualized, and analyzed. In particular, our study illuminates how increased interactivity of requirements visualization could lead to actionable decisions.

Automatic labeling of software requirements clusters

Clustering is of great practical value in retrieving reusable requirements artifacts from the ever-growing software project repositories. Despite the development of automated cluster labeling techniques in information retrieval, little is understood about automatic labeling of requirements clusters. In this paper, we review the literature on cluster labeling, and conduct an experiment to evaluate how automated methods perform in labeling requirements clusters. The results show that differential labeling outperforms cluster-internal labeling, and that hybrid method does not necessarily lead to the labels best matching human judgment. Our work sheds light on improving automated ways to support search-driven development.

A cost-benefit approach to recommending conflict resolution for parallel software development

Merging parallel versions of source code is a common and essential activity during the lifespan of large-scale software systems. When a non-trivial number of conflicts is detected, there is a need to support the maintainer in investigating and resolving these conflicts. In this paper, we contribute a cost-benefit approach to ranking the conflicting software entities by leveraging both structural and semantic information of the source code. We present a study by applying our approach to a legacy system developed by computational scientists. The study not only demonstrates the feasibility of our approach, but also sheds light on the future development of conflict resolution recommenders.

Visual analytics for software requirements engineering

The research on visual analytics for requirements engineering has noticeably advanced in the past few years. For many software projects, requirements management needs an effective and efficient path from data to decision. Visual analytics (VA) creates such a path that enables the user to extract insights by interacting with the relevant information. While various requirements visualization techniques exist, only few have produced end-to-end values to practitioners. In this research proposal, we advance the literature on visual requirements analytics by characterizing its key components and relationships. Such a characterization allows us to not only assess existing approaches, but also develop tool enhancements in a principled manner. We describe our ongoing work on VA and outline future research plans.

Conflict resolution support for parallel software development

Parallel changes, in which separate lines of development are carried out by different developments, are a basic fact of developing and maintaining large-scale software systems. Merging parallel versions and variants of source code is a common and essential software engineering activity. When a non-trivial number of conflicts is detected, there is a need to support the maintainer in investigating and resolving these conflicts. In this study, the authors present software conflict resolution recommender (scoreRec), a cost-benefit approach to ranking the conflicting software entities. The contributions of scoreRec lie in the leverage of both structural and semantic information of the source code to generate conflict resolution recommendations, as well as the hierarchical presentation of the recommendations with detailed explanations. The authors evaluate scoreRec through an industrial-strength legacy system developed by computational scientists. The results show that scoreRec offers relevant and insightful information and sound engineering support for conflict resolution. The authors- work also sheds light on the future development of recommendation systems in the context of software merging.

On the use of visual clustering to identify landmarks in code navigation

Recovering the legibility features is key to reverse engineering as the legible software systems can ease developers code navigation and comprehension. Landmarks are important legibility features that developers use as reference points. In this paper, we leverage visual clustering to explore how landmarks can be identified via static dependencies. Besides organizing software entities with coherent patterns, visual clustering offers additional insights by rigorously rendering a holistic picture of the code base to the two-dimensional space. We contribute a couple of heuristics based on the cluster layout to identify the landmark files. Our visual exploration of Eclipse Mylyn open source Java project reveals developers reliance on the landmarks during code navigation and shows the promise of using static dependencies to uncover the landmarks in the software space.

Ethnographic field work in requirements engineering

The requirements engineering (RE) processes have become a key in developing and deploying enterprise information system (EIS) for organisations and corporations in various fields and industrial sectors. Ethnography is a contextual method allowing scientific description of the stakeholders, their needs and their organisational customs. Despite the recognition in the RE literature that ethnography could be helpful, the actual leverage of the method has been limited and ad hoc. To overcome the problems, we report in this paper a systematic mapping study where the relevant literature is examined. Building on the literature review, we further identify key parameters, their variations and their connections. The improved understanding about the role of ethnography in EIS RE is then presented in a consolidated model, and the guidelines of how to apply ethnography are organised by the key factors uncovered. Our study can direct researchers towards thorough understanding about the role that ethnography plays in EIS RE, and more importantly, to help practitioners better integrate contextually rich and ecologically valid methods in their daily practices.

V R visu : a tool for virtual reality based visualization of medical data

A large body of research is available on data visualization and many tools have been developed to visually comprehend large healthcare datasets. However, little attention has been paid to the area of visualization using virtual reality (VR). In order to fill this gap, we designed and developed a tool called VRVisu to visualize large and complex medical datasets using VR. More specifically, our research focuses on creating 3D images of tumors from real-world medical datasets and displays them in a virtual reality environment in meaningful ways. The tool provides novel visualizations for medical practitioners to identify and analyze the change in shape of tumors.