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Towards an Intelligent Systems/ Software Process infrastructure for early SDLCAuthor: PADMALATA VENKATA NISTALA Date: 2022-04-13 Report no: IIIT/TH/2022/45 Advisor:Raghu Reddy AbstractA major customer demand in the software industry today is quality at speed. Systems and Software Development Lifecycle processes (SDLC) are central to the work in IT Organizations that guide product and service development and act as vehicles for building the engineering deliverables (ISO/IEC 12207, 2017; ISO/IEC 15288, 2015). Despite adopting standard processes, IT organizations continue to face enormous quality-related challenges in developing engineering deliverables primarily because the standards outline “what” tasks to be carried out but not the “how” part of developing solutions. There is a lot of reliance on the knowledge and expertise of the people involved in the SDLC to build quality solutions for the stated problem. More specifically, early SDLC phases such as proposals, requirements are document-centric and manually intensive. The essential knowledge related to the domain and solutions is buried in various kinds of documents in shared repositories. Subject Matter Experts (SME) refer to and reuse content from these documents while preparing solutions for a given problem in early SDLC. The only technological help is often a “Ctrl-F” on individual documents to search and pick up relevant information. Even though downstream SDLC phases like coding and testing have long been recognized as focus areas for productivity, and automation exists in terms of code generation and test automation, the technology and automation paradigms are under-exploited in early SDLC phases. The responsibility for solution development during the early SDLC phases lies entirely with the SMEs, and the whole process is entirely manual. There is a huge gap in process infrastructure support for the early SDLC. AI in software development lifecycle is an emerging area of research with a focus on applying intelligent tools and techniques to improve/ accelerate/ disrupt the life cycle. As part of the “AI in SDLC” paradigm, technologies such as natural language processing (NLP), rule-based reasoning are widely explored. This thesis investigates how specific processes in early SDLC can be digitally reimagined to facilitate the transformation from a manual approach to an automated approach. This thesis proposes a generic intelligent Systems/ software Process INfrastructure (iSPIN) framework focusing on extracting solution knowledge into machine-processable models and building intelligent recommender systems for a given problem in the early phases of SDLC. The key contributions of this research are: i) Literature and Field Studies on Quality Models and SDLC Practices to understand the quality challenges and establish the quality gaps in SDLC ii) iSPIN framework for building intelligent technology infrastructure for early SDLC phases iii) A generic SDLC System meta-meta-model (SDLC_MMM) for solution development iv) Case Study1: iSPIN implementation and evaluation for “Request for proposal (RFP) response” process v) Case Study2: iSPIN implementation and evaluation for “Requirements” process The major contribution of this work is the iSPIN framework with crucial building blocks to build an intelligent process infrastructure. A set of technology enablers are proposed comprising an SDLC System meta-meta-model, AI-based knowledge extraction, and intelligent solution recommender. The SDLC system meta-meta-model (SDLC_MMM) captures the essential SDLC process elements and their interactions. AI-based knowledge extraction parses the natural language (NL) documents and automatically extracts key knowledge elements with associated knowledge classification and context tagging. Finally, an intelligent solution recommender analyzes the specific problem in the SDLC phase, determines applicable concerns, recommends relevant knowledge, and automatically composes a machine-first “SDLC output” document as per the required document format providing significant productivity benefits. iSPIN is implemented as a proof of concept (PoC) technology and validated as part of two early SDLC processes for the below use cases - i) For automatically generating a Response to a clientsupplied Request for Proposal (RFP) / Request for Information (RFI) response ii) For generating context-sensitive user stories from diverse Requirements Specifications. For the first use case, The efficacy of the approach has been demonstrated on a set of RFP/RFIs. Based on the case study implementation, it has been observed that for any RFx received from the client, iSPIN can potentially generate 50%-70% of RFP/RFI response automatically. This capability of “Machine First RFP/RFI Response” is a huge value-add for the Presales teams. For the second use case, demonstrated results on two products and 90 requirement specification documents. The model extraction efficiency is very good, with 97% accuracy. While iSPIN has been conceived, developed, and validated for the early SDLC processes, the guiding principles, SDLC_MMM, and the iSPIN framework are generic. The iSPIN framework and technology can help pave the way towards realizing the dictums of “Quality at Speed” and “Digital transformation of SDLC Processes.” Full thesis: pdf Centre for Software Engineering Research Lab |
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