Requirement engineering in many IT services industries continues to be document-centric and heavily manual. Requirements specification documents contain details of product features, process flows, activities, rules, parameters, etc. Intricate knowledge of dependencies between these specification elements is necessary for effective change impact analysis. In document-centric change impact analysis, Subject Matter Experts (SMEs) have to search for information across multiple requirements specification documents. Especially with business products having multiple customized product implementation scenarios, it is crucial to consider the context (client/ operating market/ geography) when analyzing the change request (CR), that makes the overall CR impact analysis process a time-, effort- and intellect-intensive endeavor, and vulnerable to errors. To overcome these challenges, we propose Natural Language Processing (NLP) and Model-Driven Engineering (MDE) based, automated Contextual Change Request Impact Analysis (ContCRIA). ContCRIA automates change impact analysis and generates a detailed CR impact analysis report. In this paper, we describe the overall approach of ContCRIA and its application on two real-world products thus bringing out its efficacy as well as lessons learned. Though the findings are shared in the specific context of two industry products, we believe researchers, practitioners, and tool vendors will find the takeaways from this approach and experience applicable in other contexts too.

Medical Writing is an art of writing scientific documents which includes regulatory and research-related content. To obtain approval for marketing new medicines, pharmaceutical companies are obligated to provide drug authorities with a huge volume of documents related to clinical trials. Creating these clinical trial documents is a time, effort, and skill-intensive process as the required information exists in fragmented form distributed across various information sources. To overcome these challenges in medical writing, we propose Automated Medical Writing tool (AutoMW). AutoMW enables the digitalization of information from different sources of information using a meta-model-based approach and leverages these models for the automated generation of clinical trial documents as per the regulatory authority document templates. This paper describes the approach and illustrates its utility and efficacy in real-world clinical trial application of two use cases - breast cancer, and diabetes.

Typically, business applications have complex and extensive functionality. Often, rules are scattered throughout the application documentation and code, making it challenging to modify them. Because business rules change more frequently, it is preferable to externalize the rules and move them outside the application. The requirements specification can serve as a valuable source for extracting and classifying rules that can further aid in assigning the rules to the appropriate teams based on their areas of expertise and externalizing the rules in the implementation. We introduce RClassify, which extracts and classifies rules from requirements specifications spread across several NL documents into eight different business rule classes. RClassify combines NLP and ML-based classification approaches to improve classification accuracy. We discuss the implementation of this approach in three real-world large-size and complex products, demonstrating its effectiveness, experience, and lessons learned. While the findings are presented in the specific context of three industry products, we believe that researchers, practitioners, and tool vendors will find the insights and experiences gained from this approach applicable in other contexts.

Requirement engineering in many IT services industries continues to be a document-centric and heavily manual activity, relying on the expertise of business analysts. Requirement specification documents contain details of product features, process flows, activities, rules, parameters, etc. Intricate knowledge of dependencies between these specification elements is necessary for carrying out the effective evolution of the product over time. Today, Business Analysts (BA) are forced to recourse to keyword-based search across multiple requirement specification documents which is a time-, effort-and intellect-intensive endeavor, and vulnerable to the errors of omission and commission. To overcome these lacunae, we propose DizSpec, an automated approach for digitalizing the requirement specification documents into a model form through automatic extraction of specification model elements and the various dependencies between them. The proposed approach creates a digital thread providing machine-processable traceability from product features to its specification elements. It also provides an easy natural language querying mechanism to generate traceability and impact analysis reports of interest. In this paper, we describe the application of this approach to two real-world products thus bringing out its efficacy as well as lessons learned from this transformation journey of the document-centric process to a model-centric and automated process. Though the findings are shared in the specific context of two industry products, we believe, researchers, practitioners, and tool vendors will find the takeaways from this approach and experience applicable in other contexts too.

Software quality has become the lever of differentiation in today's competitive marketplace. Quality at speed is the customer demand and automation is the biggest bottleneck holding the evolution of quality function. Increased levels of automation and intelligence in software engineering are the emerging trends across the IT field. As systems and software processes guide the life cycle activities and are the vehicles for building quality, it is necessary to look at the process infrastructure for the extent of process automation support provided and the digital enablement. This paper maps out the existing process infrastructure support in industry practice and proposes a roadmap for digital re-imagination of software and systems processes. Harmonizing the quality engineering themes with digital technologies, we propose a framework for building an intelligent software process infrastructure, iSPIN that can help in digital re-imagination of software and systems lifecycle processes. The framework has been implemented using digital technologies and has been piloted with one of the industry business unit for re-imagination of "proposal process". The proposed iSPIN framework will help in unprecedented automation and quality engineering at each process step and paves the way towards realizing the dictums of "Quality at Speed" and "Digital transformation of Software Process".

Quality Models play a critical role in assuring quality and have evolved over 40+ years. They provide support for defining quality attributes, building and measuring the quality of the resulting product. Each quality model adopts a critical view on quality in terms of a set of model elements and relationships between them. This study aims to provide an overview of the state-of-the-art research on quality models with a focus on encompassing model elements and their support to architecting quality. The study was conducted using systematic mapping as the research methodology. A total of 238 primary papers were classified based on the type of research, standards usage, and publication trends. We identified that 17% (40) of papers belong to quality models. These 40 models were analyzed for the underlying meta-model elements and their support for a quality architecture using Bayer's reference architecture framework. The architecture phase mapping analysis shows that quality planning phase is 100% supported, quality assessment is 75% supported, quality documentation is included in 40% models and quality realization aspect is barely considered in 13% models. Quality realization happens through software processes and patterns, and it is necessary to evolve quality models and software process architectures that correlate quality definitions and quality realization mechanisms. Future research is expected in this direction.

Requirements management and traceability have always been one of grand challenges in software development area. Studies reveal that 30-40% of software defects can be traced to gaps or errors in requirements Although several models and techniques have been defined to optimize the requirements process, ensuring alignment and consistency of elicited requirements continues to be a challenge. All software engineering standards and methodologies recognize the importance of maintaining relationships among the software elements for traceability. We have leveraged the structured relationships among the requirement elements to come up with an approach to systematically carry out consistency analysis of requirements for software systems. The framework has multiple models: a multi layered requirement model, a configuration structure to link and track the requirement items, a consistency analysis method to identify the inconsistencies in the requirements and a consistency index computation to indicate the level of consistency in overall requirements of the software system. This approach helps to validate the requirements from both completeness and correctness perspectives and also check their consistency in forward and backward directions. The paper outlines the framework, describes the encompassing models and the implementation details from pilot of the framework to an industry case study along with results.