Large organizations respond to numerous volumes of Request for Proposals (RFPs) annually. The process of responding to RFPs is largely a manual and time-, effort-, and intellect-intensive endeavor, and vulnerable to errors. To bridge these gaps, we present RFPAnaFit, a tool designed for RFP Analysis, Fitment evaluation, and automated response generation. RFPAnaFit provides an automated solution for generating responses to RFPs. RFP typically comprises a set of questions that outline the requirements for various capabilities that need to be addressed. It leverages product capability documents and uses multiple techniques to assess the fitment of RFP requirements against product capabilities and generate RFP response document. We have tested RFPAnaFit on a real-world, large-sized product with two RFPs, demonstrating its effectiveness and providing valuable insights. While the findings are shared within the context of an industry-specific product, we believe that researchers and practitioners will find the lessons learned from this approach applicable to other domains as well.

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.

Enterprise application development faces significant challenges, with each phase of the software development life cycle (SDLC) requiring experts with specific skills. The expertise of the individuals involved, greatly affects the quality and speed of work in each phase. The large size and complexity of modern software systems further exacerbates these problems. Recently, there has been a growing interest in using Generative AI (GenAI) techniques for software engineering tasks. GenAI can help Subject Matter Experts (SMEs) work more efficiently and can help in overcoming skill barriers. By leveraging GenAI, SMEs can save significant time and effort. This paper introduces meta-model based prompting approach to generate enterprise application code leveraging large language models (LLMs). Prompts help in the refinement of input requirements into refined requirements and design specifications using LLMs, ultimately generating code from these specifications. We share our approach and results of applying approach to generate small yet complex applications.

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.

Environmental uncertainties and hyperconnectivity force technosocio-economic systems to introspect and adapt to succeed and survive. Current practices in decision-making are predominantly intuition-driven with attendant challenges for precision and rigor. We propose to use the concept of digital twins by combining results from Modelling & Simulation, Artificial Intelligence, and Control Theory to create a risk free 'in silico' experimentation aid to help: (i) understand why a system is the way it is, (ii) be prepared for possible outlier conditions, and (iii) identify plausible solutions for mitigating the outlier conditions in an evidence-backed manner. We use reinforcement learning to systematically explore the digital twin solution space. Our proposal is significant because it advances the effective use of digital twins to new problem domains that have new potential for impact. Our approach contributes an original meta model for simulatable digital twin of industry scale techno-socioeconomic systems, agent-based implementation of the digital twin, and an architecture that serves as a risk-free experimentation aid to support simulation-based evidence-backed decision-making. We also discuss the rigor of our validation of the proposed approach and associated technology infrastructure through a representative sample of industry-scale real-world use cases.

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.

Chemical manufacturers, importers, or distributors produce a Safety Data Sheet (SDS) for every chemical they use based on the Globally Harmonized System of Classification and Labelling of Chemicals(GHS). SDS is essential to adequately deliver information about the hazards of the particular chemical during its usage and handling. Authoring GHS-compliant SDS is very costly. Furthermore, revisions in GHS add, delete or modify the contents of an SDS. A pre-existing SDS therefore also needs to be adapted to changes in the GHS. Automation in the generation and checking of SDS per GHS would results in substantial savings in cost and effort. Additionally, if the SDS pre-exists, we ensure that all requisite label elements as per GHS are present in SDS, including precautionary statements, hazard statements, signal words, and pictograms. Our approach uses a versioned graph database, and image processing to generate, check, and keep an SDS compliant with the GHS. We demonstrate our approach with a pre-existing SDS of a chemical called DOWANOL™PM High Purity Grade, complying with GHS versions 7 and 8.

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".

Modern enterprises are large complex systems operating in highly dynamic environments thus requiring to respond quickly to a variety of change drivers. Moreover, they are systems of systems wherein understanding is available in localized contexts only and that too is typically partial and uncertain. With the overall system behaviour hard to know a-priori and conventional techniques for system-wide analysis either lacking in rigour or defeated by the scale of the problem, the current practice often exclusively relies on human expertise for monitoring and adaptation. We outline a knowledge-guided simulation-aided data-driven model-based evidence-backed approach to make enterprises adaptive. The approach hinges on the concept of Digital Twin - a set of relevant models that are amenable to analysis and simulation. We describe the core modeling and model processing infrastructure developed, and early stage explorations of its application to problems where the mechanistic world view holds. We argue similar benefits are possible for problem spaces involving human actors as well.

In spite of the proliferation of the business process and data compliance checking approaches, in practice, regulatory compliance management still demands considerable manual intervention. Previous research in the field of compliance has established that the manual specification/tagging of the regulations not only fails to ensure their proper coverage but also negatively affects the turnaround time both in proving and maintaining the compliance. Our contribution is an (semi-) automated transformation of the legal NL (English) text to SBVR Model via authoring of Structured English (SE) rules. The key benefit of our approach is the direct involvement of the domain experts to specify regulations using SE, which is close to English, rather than a formal specification language. We substantiate the approach using an example from industry regulations in banking and financial services domain.

Effective decision-making of modern organisation requires deep understanding of various aspects of organisation such as its goals, structure, business-as-usual operational processes etc. The large size and complex structure of organisations, socio-technical characteristics, and fast business dynamics make this decision-making a challenging endeavour. The state-of-practice of decision-making that relies heavily on human experts is often reported as ineffective, imprecise and lacking in agility. This paper evaluates a set of candidate technologies and makes a case for using actor based simulation techniques as an aid for complex dynamic decision-making. The approach is justified by enumeration of basic requirements of complex dynamic decision-making and the conducting a suitability of analysis of state-of-the-art enterprise modelling techniques. The research contributes a conceptual meta-model that represents necessary aspects of organisation for complex dynamic decision-making together with a realisation in terms of a meta model that extends Actor model of computation. The proposed approach is illustrated using a real life case study from business process outsourcing industry

Modern enterprises operate in an unprecedented regulatory environment where increasing regulation and heavy penalties on non-compliance have placed regulatory compliance among the topmost concerns of enterprises worldwide. Previous research in the field of compliance has established that the manual specification of the regulations used by GRC frameworks not only fails to ensure their proper coverage but also negatively affects the turnaround time both in proving and maintaining the compliance. Our key contribution in this paper is an implementation of a controlled natural English like (domain-specific) language that can be used by domain experts to specify regulations for automated compliance checking. We demonstrate this language using examples from industry regulations in banking and financial services domain.

Many enterprise systems are document intensive that requires extensive manual verification in the form of maker and checker. However, a maker-checker based verification raises several challenges with respect to increase in cost and time of verification. Furthermore, any manual labor intensive verification is not free from human oversight and can lead to costly errors. Therefore, to alleviate the challenges arising out of human verification of document intensive systems, we propose a rule based framework that enables automatic verification of document based systems. The framework uses ontology based knowledge representation techniques along with appropriate natural language processing methods to extract operational rules from business documents and then use suitable reasoning engine for verification. The above framework is validated in the light of a real life case study namely International Trade that deals with several critical financial documents like Letter-of-Credit, Bill-of-Lading, Commercial Invoice etc.

Modern enterprises face an unprecedented regulatory regime. Traditional compliance practices in enterprises rely heavily on domain experts whose judgement determines what compliance means and how to reflect regulations onto the enterprise processes and data to make them compliant. These activities are mostly manual in nature. We present a machine learning approach to modeling compliance. Our key innovations are a) use of active learning- a semi-supervised system capable of learning interactively from the domain expert to identify regulations and b) informing the feature representation of the active learner based on domain- specific entities and relations to effectively build a domain model of regulations. Early results show that our system reduces the burden on domain experts to a large extent, enables latching domain expert's knowledge, and makes further steps in compliance easier by the use of models.

Knowledge is manifested in an enterprise in various forms ranging from unstructured operational data, to structured information like programs, as well as relational data stored in databases to semi-structured information stored in XML files. This information embodies the core of an enterprise knowledge base and analyzing the knowledge base can result in intelligent decision making. In order to realize this goal we begin with representing and analyzing unstructured knowledge present in an enterprise. In particular, this paper presents a real life example of a document intensive business process (International Trade) and attempts to model and analyze the process in a formal way. Typically, the information contained in a document intensive business process is of operational nature and requires extensive manual verification, which is both time consuming and error prone. Therefore, this research aims to eliminate such exhaustive manual verification by constructing a knowledge base in the form of ontology and apply suitable rule based reasoners to automate the verification process.

Enterprise agility, generally defined as the ability of an enterprise to detect and respond to changes timely and effectively, is a core imperative for effective change management and optimal performance in contemporary enterprises. It can improve operational efficiency, and enhance competitive ability. At the same time, it is elusive, challenging, and difficult to achieve. A data centric approach can support change management process by providing an avenue to capture, store, and manage information, activities, and knowledge relating to changes. In addition, it can provide an avenue for re-using previous (successful) change management strategies to adapt to similar changes in the future. In this paper, we examine change management concepts and requirements, integrate them into a conceptual data model. To demonstrate utility of this data model, we apply it to real world industry case study. Results show that this approach can be useful to enterprise change management by providing information and intelligence to support change management decisions.

Models are an important tool in conquering the increasing complexity of modern software systems. Key industries are strategically directing their development environments towards more extensive use of modeling techniques. MiSE 2015 aimed to understand, through critical analysis, the current and future uses of models in the engineering of software-intensive systems. The MiSE workshop series has proven to be an effective forum for discussing modeling techniques from both the MDE and software engineering perspectives. An important goal of this workshop is to foster exchange between these two communities. In 2015 the focus was on considering the current state of tool support and the challenges that need to be addressed to improve the maturity of tools. There was also analysis of successful applications of modeling techniques in specific application domains, with attempts to determine how the participants' experiences can be carried over to other domains.

This paper takes a practitioner's perspective on the problem of organisational decision-making. Industry practice follows a refinement based iterative method for organizational decision-making. However, existing enterprise modelling tools are not complete with respect to the needs of organizational decision-making. As a result, today, a decision maker is forced to use a chain of non-interoperable tools supporting paradigmatically diverse modelling languages with the onus of their co-ordinated use lying entirely on the decision maker. This paper argues the case for a model-based approach to overcome this accidental complexity. A bridge meta-model, specifying relationships across models created by individual tools, ensures integration and a method, describing what should be done when and how, and ensures better tool integration. Validation of the proposed solution using a case study is presented with current limitations and possible means of overcoming them outlined.

Modern enterprises face an unprecedented regulatory regime. Industry governance, risk, and compliance (GRC) solutions are document-oriented and expert-driven. Formal compliance checking techniques in contrast attempt to provide ways for rigorous modeling and analysis of regulatory compliance but miss out on holistic GRC perspective due to missing integration between diverse set of (semi-) formal models. We show that streamlining regulatory compliance using multiple purposive models of various aspects of regulations, it is possible to leverage both the rigor of formal techniques and the holistic enterprise GRC perspective. Our contributions are twofold. First, we present a model-driven architecture based on a conceptual model of integrated GRC that is capable of addressing key challenges of regulatory compliance. Second, using Know Your Customer regulations in Indian context as a case study, we demonstrate the utility of this architecture. Initial results with KYC regulations are promising and point to further work in model-driven regulatory compliance.

With internet becoming increasingly pervasive, data analytics is playing increasingly critical role in the business. Data to be analyzed exists in large quantity and in multiple formats. Many technologies exist to support Big Data analytics. However, they remain somewhat of a challenge for average developer to use. It's been seen that model-driven development (MDD) approach can eliminate accidental complexity to a large extent. We discuss MDD approach for development of MapReduce based Big Data applications, its efficacy and lessons learnt.

We share experience in supporting development and evolution of a large banking product using a homegrown model driven engineering (MDE) toolset. We discuss improvements that needed to be introduced in the MDE toolset to support collaborative development with teams distributed across different geographical locations. Though experience is shared in a specific context, we believe, MDE researchers, enthusiasts, practitioners and tool vendors will find the takeaways from this experience applicable even in a more general context of large scale software development.

Taking and executing cost effective decisions in enterprises is becoming increasingly difficult due to multiple change drivers that affect varied aspects of enterprise. Enterprise architecture (EA) frameworks provide holistic treatment of whats and hows of enterprise but leave the important questions of whys unaddressed. Intentional modeling and system dynamics modeling provide treatment of whys at a point in time and over time respectively. We propose an approach where both intentional and system dynamics models are used in conjunction with EA models for a more effective treatment of whys than provided by either. Initial results with a case study suggest that best of both worlds may be obtained with such combined treatment of whys in

Industry has been practicing model-driven development in various flavours. In general it can be said that modelling and use of models have delivered on the promises of platform independence, enhanced productivity, and delivery certainty as regards development of software-intensive systems. Globalization market forces, increased regulatory compliance, ever-increasing penetration of internet, and rapid advance of technology are some of the key drivers leading to increased business dynamics. Increased number of factors impacting the decision and interdependency amongst the key drivers is leading to increased complexity in making business decisions. Also, enterprise software systems need to commensurately change to quickly support the business decisions. The paper presents synthesis of our experience over a decade and half in developing model-driven development technology and using it to deliver several business-critical software systems worldwide.

We discuss our experience of using model-based techniques to generate model-based code generators. The central idea behind model-driven development (MDD) is to use suitable models to specify various concerns and transform these models to a variety of text artifacts. A business product needs to deliver a given business functionality on a wide variety of implementation platforms and architectures thus necessitating multiple sets of code generators. However, there is a considerable commonality across these code generators. In absence of a suitable abstraction for capturing this commonality, there is little or no reuse across these code generators. We present an abstraction for organizing model-based code generators as a hierarchical composition of reusable building blocks. A building block is a localized specification of a concern in terms of a concern-specific meta model, model to model trans-formation, and model to text transformation. Model-based code generation is a 3-step walk over the composition tree wherein the first step transforms individual concern-specific models into a unified model, the second step transforms the unified model into individual concern-specific text artifacts, and the third step composes these text artifacts.

We discuss our experience of introducing MDA approach, supported by our MDA toolset, in an IT consultancy and software development services organization. Initially, our MDA toolset provided a set of modeling notations for specifying different layers of an enterprise application and a set of code generators that transform these models into platform-specific implementations. In spite of considerable amount of code being generated, it didn't translate into commensurate productivity gain over the entire development lifecycle of large projects. What was missing was support for collaborative development. Providing suitable abstractions and tool support for a collaboration-based development method improved the situation considerably. Enterprise product lines and large development projects benefited the most resulting in improved productivity, better code quality, platform independence and easier change management. However, the steep learning curve and inadequate debugging support for models were significant hurdles. Small to medium sized projects found this MDA approach too heavy and restrictive, and preferred a flexible and lightweight meta-data driven aspect-oriented approach. Our experience suggests that MDA will find greater acceptance if complimented with well-defined and flexible methodologies that support collaborative development.