Leveraging Latent Natural Language Processing Techniques for User Story Management in Agile Software Development

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context:</i> User stories have been widely accepted as artifacts to capture the user requirements in agile software development. They are short pieces of texts in a semi-structured format that express requirements. Natural language processing (NLP) techniques offer a potential advantage in user story applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Conduct a systematic literature review to capture the current state-of-the-art of NLP research on user stories. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method:</i> The search strategy is used to obtain relevant papers from SCOPUS, ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar. Inclusion and exclusion criteria are applied to filter the search results. We also use the forward and backward snowballing techniques to obtain more comprehensive results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> The search results identified 718 papers published between January 2009 to December 2020. After applying the inclusion/exclusion criteria and the snowballing technique, we identified 38 primary studies that discuss NLP techniques in user stories. Most studies used NLP techniques to extract aspects of who, what, and why from user stories. The purpose of NLP studies in user stories is broad, ranging from discovering defects, generating software artifacts, identifying the key abstraction of user stories, and tracing links between model and user stories. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> NLP can help system analysts manage user stories. Implementing NLP in user stories has many opportunities and challenges. Considering the exploration of NLP techniques and rigorous evaluation methods is required to obtain quality research. As with NLP research in general, the ability to understand a sentence’s context continues to be a challenge.

Agile development practices have been widely adopted in a variety of organizations, yet only a few tools are available to help make the practical process of managing agile teams less painful and more successful. HP Agile Manager is a purpose-built SaaS-based Agile planning tool. HP Agile Manager provides a simpler, smarter way to manage collaborative development. Liran Tal provides a practical, concise approach to using Agile Manager in a variety of settings to better plan, conduct, and manage software releases within development teams. His step-by-step approach will show you how to plan your products features, streamline the agile sprint process, work with user stories, and track defects throughout the development process. Agile Manager can work for small startups, mid-sized teams, as well as scale up for bigger organizations as a cost-effective and flexible tool to apply agile techniques to improve your software development process. What youll learnHow to apply the concepts of agile management in software development teams to better execute and increase performance and delivery How a software release cycle works, how to manage sprints, set timelines, build a products backlog, and manage day to day team tasks How to leverage agile best practices to guide your team through effective release planning and team collaboration Understand and apply agile software delivery concepts such, estimating and prioritizing user stories. Streamlining sprints planning, execution, and retrospective How to setup, and use HP Agile Manager to effectively manage software lifecycle through agile methodology, and learn how to leverage Agile Manager's flexible workflow to meet your team requirements and processes Using Agile Manager's dashboards for real-time view into the current status of releases, and sprints to gain insights and pro-actively respond to changes, as well as tracking your team progress, work-load, and keeping an eye on tasks burn-down, or cumulative flow Who this book is for Developers, quality engineers, development team leaders and new managers will find this book to serve them well in learning and applying agile methodologies in their teams.

In Agile software development, user stories play a vital role in capturing and conveying end-user needs, prioritizing features, and facilitating communication and collaboration within development teams. However, automated methods for evaluating user stories require training in NLP tools and can be time-consuming to develop and integrate. This study explores using ChatGPT for user story quality evaluation and compares its performance with an existing benchmark. Our study shows that ChatGPT’s evaluation aligns well with human evaluation, and we propose a “best of three” strategy to improve its output stability. We also discuss the concept of trustworthiness in AI and its implications for non-experts using ChatGPT’s unprocessed outputs. Our research contributes to understanding the reliability and applicability of Generative AI in user story evaluation and offers recommendations for future research.

Today, the most vital step is software testing, especially in product quality. Agile software development is exceedingly difficult and has contentious issues in literature and business. Since most Agile techniques place more emphasis on development than software testing activities, different people have varying opinions about how software testing fits into these methodologies. Agile places a high emphasis on frequent deliveries and tight client connections. However, software testing is complex because agile does not incorporate many destructive testing techniques typically required for a product of higher-quality. The study reveals industry-wide Agile testing procedures and the main problems that arise when Agile development is used. Additionally covered are the differences between manual and automated testing, best automation practices, and ways to lead different testing teams in an agile testing environment. This study covers every facet of the Agile software testing process. Agile development has shown that testing is not a distinct stage but rather a crucial component of software development along with the coding. The agile team employs an overall strategy to produce software products of the highest quality. As part of an agile team, testers collect customer samples of the desired behaviour and collaborate with the developer's team to help the coders translate these examples into executable specifications. Coding and testing are done iteratively and incrementally, developing every item until it is valuable enough to release in production. It addresses several testing modalities. The classification provided by its quadrants aids teams in determining and organizing the necessary tests. To increase quality assurance, this article will examine various and improved Agile Testing Processes and methodologies. Additionally, the topic of Agile development's software testing method is covered. Agile development processes can be improved and more advantageous by incorporating testing procedures.

Agile development has become increasingly popular among software development teams due to its capacity to deliver and update software rapidly while accommodating evolving requirements. Within this dynamic context, access control policies are critical for ensuring the security of systems by defining who can access specific resources under given conditions. However, identifying and documenting these policies often rely on manual, time-intensive processes prone to errors and oversight. This paper proposes an innovative framework leveraging Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to automate the extraction and organization of access control policies from user stories and software documentation. The framework focuses on the early stages of the development lifecycle, capturing access control requirements as expressed in natural language artifacts. It comprises two core components: (1) a pipeline for extracting and categorizing access control policies, enabling precise mappings between roles, actions, and resources, and (2) an interactive chatbot designed to support Security Operations Center (SOC) analysts in evaluating suspicious access requests by providing contextualized insights into access policies. By integrating advanced natural language processing techniques with retrieval-based augmentation, the framework aims to reinforce access control mechanisms by improving visibility, and providing contextualized insights for security analysts.

User stories are commonly used to capture user needs in agile methods due to their ease of learning and understanding. Yet, the simple structure of user stories prevents us from capturing relations among them. Such relations help the developers to better understand and structure the backlog items derived from the user stories. One solution to this problem is to build goal models that provide explicit relations among goals but require time and effort to build. This paper presents a pipeline to automatically generate a goal model from a set of user stories by applying natural language processing (NLP) techniques and our initial heuristics to build realistic goal models. We first parse and identify the dependencies in the user stories, and store the results in a graph database to maintain the relations among the roles, actions, and objects mentioned in the set of user stories. By applying NLP techniques and several heuristics, we generate goal models that resemble human-built models. Automatically generating models significantly decreases the time spent on this tedious task. Our research agenda includes calculating the similarity between the automatically generated models and the expert-built models. Our overarching research goals are to provide i. an NLP-powered framework that generates goal models from a set of user stories, ii. several heuristics to generate goal models that resemble human-built models, and iii. a repository that includes sets of user stories, with corresponding human-built and automatically generated goal models.

A user story is commonly applied in requirement elicitation, particularly in agile software development. User story is typically composed in semi-formal natural language, and often follow a predefined template. The user story is used to elicit requirements from the users' perspective, emphasizing who requires the system, what they expect from it, and why it is important. This study aims to acquire a comprehensive understanding of user stories in requirement elicitation. To achieve this aim, this systematic review merged an electronic search of four databases related to computer science. 40 papers were chosen and examined. The majority of selected papers were published through conference channels which comprising 75% of total publications. This study identified 24 problems in user stories related to requirements elicitation, with ambiguity or vagueness being the most frequently occurring problem reported 18 times, followed by incompleteness reported 11 times. Finally, the model approach was the most popular approach reported in the research paper, accounting for 30% of the total approaches reported.

User stories that are the requirements engineering artifacts in agile software development must be accepted by the end user before being implemented. Acceptance testing is used to confirm the acceptance of user stories. User story acceptance tests are driven by user-defined acceptance criteria. The number of acceptance tests increases as the application size increases. One of the prominent reasons for adopting agile software development is quicker delivery of working software. In this paper, we attempt to prioritize acceptance tests in order to identify critical tests. Execution of critical acceptance tests is sufficient to satisfy the acceptance criteria for a user story and reduces the time to delivery of software. Prioritization of acceptance tests is realized by application of meta-heuristic techniques, i.e., genetic algorithm (GA), cuckoo search algorithm, and micro-GA algorithm. The information flow (IF) model is used as a basis of fitness function to ensure maximum coverage of user acceptance criteria. We demonstrate the applicability and effectiveness of the proposed approach with the help of a realistic example. A comparative analysis of the application of meta-heuristic techniques is performed to choose the best one.

A survey of 108 agile practitioners revealed that user stories are the most widely used method for capturing requirements. However, user stories can be interpreted differently by different stakeholders, leading to potential misunderstandings within the development team. Additionally, the interconnectedness of user stories poses challenges during the requirement elicitation process. A Systematic Literature Review (SLR) of 27 articles about user story elicitation process were selected and these are examined to determine user story quality. This research will provide a comprehensive summary of user story elicitation approaches and their application in addressing user story quality issues. The study will also offer insights into selecting appropriate approaches for resolving challenges in user story requirement elicitation. Finally, most user story elicitation approach primarily focus on addressing the issue of ambiguity.

Most agile practitioners employ user stories for capturing requirements, also thanks to the embedding of this notation within development and project management tools. Among user story users, circa 70% follow a simple template: As a role, I want to action, so that benefit. User stories’ popularity among practitioners and their template-based structure make them ideal candidates for the application of natural language processing techniques. In our research, we have found that circa 50% of real-world user stories contain easily preventable linguistic defects. To mitigate this problem, we have created tool-supported methods that facilitate the creation of better user stories. This tutorial combines previous work of the RE-Lab@UU into a pipeline for working with user stories: (1) The basics of creating user stories and their use in requirements engineering; (2) How to improve user story quality with the Quality User Story Framework and the AQUSA tool; (3) How to generate conceptual models from user stories using the Visual Narrator tool and analyze them for possible ambiguity and inconsistency; and (4) How to link requirements to architectures via the RE4SA model. Our approach is demonstrated with results obtained from 20+ software companies employing user stories.

A Supporting Tool for Requirements Prioritization Process in Agile Software Development

Stakeholder involvement is vital in the requirements elicitation process. However, a lack of stakeholder involvement in the project often occurs. Various data sources, such as business processes and software documentation, will help determine what stakeholders need. The limitation of these documents is that they are technical and do not contain numerous stakeholder's needs. This paper aims to extract information related to software requirements from online news using domain specificity. The online news comprises elements that compose the user story, namely: the aspect of who (stakeholder), the aspect of what (functional), and the aspect of why (reason). User stories are standard practices used to capture and write functional software requirements in agile software development. This paper proposes to use domain specificity to extract aspects of what from online news to increase understanding of domain knowledge, especially software functionality. We use SRS documents and online news as data sources. Based on these datasets, domain specificity is calculated to produce software specific vocabulary. POS tags are being used to extract software requirements-related information from online news. When examining two datasets, our approach improved precision and recall with average values of 0.56 and 0.579.

[Context] In agile software development, requirements are frequently represented in lightweight artifacts such as user stories (USs). To support their validation, USs may be complemented with additional artifacts such as acceptance criteria and test cases. [Problem] Existing studies propose frameworks for US writing, or investigate high-level factors that contribute to productivity, such as teamwork and support from management. However, empirical evidence on the impact of requirements artifacts on team efficiency is scarce. [Method] Through a mixed-methods case study at a large Dutch organization, we investigate how the use of requirements artifacts affects efficiency. We collected data from eight agile teams: 1,345 USs from nineteen sprints, US quality indicators, the quantity of associated acceptance criteria and test cases, and cycle time. We statistically analyzed the existence of correlations between these variables, and we triangulated our results through interviews with product owners and scrum masters from the involved teams. [Findings] Our employed indicators for US quality do not seem to have an effect on on-time completion, with our interviewees highlighting the trade-off between investing on writing quality and short-term efficiency. We found, instead, that the existence of acceptance criteria may be a relevant factor for on-time completion, as confirmed both by statistical tests and the interviewees.

Software maintenance is one of the most debated phases in software development process for so many years. Having reputed as the most expensive phase of software development life cycle (SDLC), it utilizes the maximum share of the overall project costs as well as time. Agile software development provides opportunities to assess the direction of a project throughout the development lifecycle. However, it does not ideally map with the existing software maintenance process. One of the highlighted issues is the difficulty for searching of information as well as lack of knowledge to solve the maintenance problems within certain time frame. Thus, the main objective of this study is to improve the governance of software maintenance process in an Agile development team. In doing so, a tool named Axita is developed to assist the software maintenance team for storing of information in central data repository and managing projects in more efficient and timely manner. Based on the literature review as well as mapping between the agile software development and the existing ISO software maintenance process, we also proposed six best practices to better govern the software maintenance process in an Agile development team, to overcome the difficulty of information finding and reduce the time spent to solve the maintenance issues. We believe that our study and findings complement the efforts that have been put forth in improving the way we manage software maintenance thus enhance the efficiency of the software development process. Â

Context: Agile software development (ASD) uses ‘agile’ artefacts such as user stories and product backlogs as well as ‘non-agile’ artefacts, for instance designs and test plans. Rationales for incorporating especially non-agile artefacts by an agile team mainly remain unknown territory. Goal: We start off to explore influences on artefacts usage, and state our research question as: To what extent does maturity relate to the usage of artefacts in ASD in software product organizations? Method: In our multiple case study 14 software product organizations were visited where software product management maturity was rated and their artefacts usage listed. Results: We found maturity to be negatively correlated with the non-agile/all artefacts ratio. In other words, the more mature software product management is, the fewer non-agile artefacts are used in ASD. Conclusions: This suggests that an organizational factor influences an agile team in its artefacts usage, contradictory to the concept of self-organizing agile teams.