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Towards digitalization of requirements: generating context-sensitive user stories from diverse specifications

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Abstract

Requirements Engineering in the industry is expertise-driven, heavily manual, and centered around various types of requirement specification documents being prepared and maintained. These specification documents are in diverse formats and vary depending on whether it is a business requirement document, functional specification, interface specification, client specification, and so on. These diverse specification documents embed crucial product knowledge such as functional decomposition of the domain into features, feature hierarchy, feature types and their specific feature characteristics, dependencies, business context, etc. Moreover, in a product development scenario, thousands of pages of requirement specification documentation is created over the years. Comprehending functionality and its associated context from large volumes of specification documents is a highly complex task. To address this problem, we propose to digitalize the requirement specification documents into processable models. This paper discusses the salient aspects involved in the digitalization of requirements knowledge from diverse requirement specification documents. It proposes an AI engine for the automatic transformation of diverse text-based requirement specifications into machine-processable models using NLP techniques and the generation of context-sensitive user stories. The paper describes the key requirement abstractions and concepts essential in an industrial scenario, the conceptual meta-model, and DizReq engine (AI engine for digitalizing requirements) implementation for automatically transforming diverse requirement specifications into user stories embedding the business context. The evaluation results from digitalizing specifications of an IT product suite are discussed: mean feature extraction efficiency is 40 features/file, mean user story extraction efficiency is 71 user stories/file, feature extraction accuracy is 94%, and requirement extraction accuracy is 98%.

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Abbreviations

AI:

Artificial intelligence

BRS:

Business requirement specification

BSD:

Business specification document

BU:

Business unit

CIM:

Component independent model

CP:

Client partner

CR:

Change request

CRUD:

Create-retrieve-update-delete

DizReq:

AI engine for digitalizing requirements

FA:

Feature area

FSD:

Functional specification document

INT:

Interface specification document

IO:

Input- output

MDA:

Model driven architecture

MF:

Major feature

MSD:

Market specification document

NL:

Natural language

NLP:

Natural language processing

NLP4RE:

NLP for requirements engineering

PIM:

Platform independent model

POS:

Part-of-speech

PSD:

Product specification document

PSM:

Platform specific model

RE:

Requirements engineering

RPT:

Report specification document

SF:

Sub feature

SRS:

Software requirement specification

UML:

Unified modeling language

URS:

User requirement specification

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Authors and Affiliations

  1. TCS Research, Tata Consultancy Services, Hyderabad, India

    Padmalata V. Nistala

  2. TCS Research, Tata Consultancy Services, Pune, India

    Asha Rajbhoj, Vinay Kulkarni & Shivani Soni

  3. International Institute of Information Technology, Hyderabad, India

    Kesav V. Nori & Raghu Reddy

Authors
  1. Padmalata V. Nistala
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  2. Asha Rajbhoj
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  3. Vinay Kulkarni
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  4. Shivani Soni
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  5. Kesav V. Nori
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  6. Raghu Reddy
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Correspondence to Padmalata V. Nistala.

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Nistala, P.V., Rajbhoj, A., Kulkarni, V. et al. Towards digitalization of requirements: generating context-sensitive user stories from diverse specifications. Autom Softw Eng 29, 26 (2022). https://doi.org/10.1007/s10515-022-00324-2

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