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Scrum, a frame embedded in agile principles, was firstly developed for software development systems. Still, its principles and practices have set up operations far beyond its origins and are being espoused in a wide range of diligence. The success of Scrum in these different surrounds has led experimenters, interpreters, and associations to explore its eventuality for enhancing product performance and team invention. The core principle of Scrum revolves around the con- formation of cross functional, tone-organizing brigades, and the iterative and incremental delivery of work in short time frames known as” sprints.” Scrum provides a frame that empowers brigades to acclimatize to changing conditions, encourages collaboration, and emphasizes nonstop enhancement. These aspects make it a compelling subject for exploration into its goods on product performance. In an period characterized by fleetly evolving technological geographies and ever-adding demands for invention and effectiveness, effective cooperation has surfaced as a foundation of organizational success. Within the realm of design operation and software development, the need for high-performing brigades has noway been further critical. In response to this demand, the Scrum frame has gained significant elevation as a methodology that promises to enhance product performance and optimize design issues. [ABSTRACT FROM AUTHOR]

Background: In Agile software development, an essential initial stage is eliciting software requirements. This process engages stakeholders to achieve comprehensive results. However, a common issue is the variance in domain and technical knowledge among stakeholders, potentially impacting the quality of software requirements elicitation. Objective: Understanding the characteristics of user stories produced by stakeholders becomes crucial, particularly considering the differences in domain and technical knowledge. This study aims to compare the characteristics of user stories generated by stakeholders with varying backgrounds in domain and technical expertise. Methods: The initial step involves categorizing respondents into distinct stakeholder groups. Three stakeholders are involved in this study, constituting a combination of those with high and low technical and domain knowledge. Subsequently, data collection of user stories is conducted across various case studies. Finally, the acquired user stories are analyzed for further insights. Results: The analysis reveals variations in user stories generated by the three stakeholder categories across the three case studies. Stakeholders with domain knowledge tend to focus on 'what' aspects with task elements and 'why' aspects with hard-goal elements. Meanwhile, technical knowledge crafts user stories with capability elements in the 'what' aspect. Utilizing the QUS framework, it is evident that technical knowledge consistently produces a higher number of high-quality user stories across all quality categories. Conclusion: The contribution offered by this study lies in determining the distinct characteristics of user stories produced by different types of stakeholders, focusing on disparities in domain and technical knowledge. The study highlights the comparison of various characteristics of user story elements, such as hard-goals, soft-goals, tasks, or capabilities, and assesses the quality of user stories based on the user story framework. Additionally, it endorse the importance of process innovation in shaping the requirements gathering process and subsequently influencing the quality of user stories. [ABSTRACT FROM AUTHOR]

Agile development aims at rapidly developing software while embracing the continuous evolution of user requirements along the whole development process. User stories are the primary means of requirements collection and elicitation in the agile development. A project can involve a large amount of user stories, which should be clustered into different groups based on their functionality's similarity for systematic requirements analysis, effective mapping to developed features, and efficient maintenance. Nevertheless, the current user story clustering is mainly conducted in a manual manner, which is time-consuming and subjective to human bias. In this paper, we propose a novel approach for clustering the user stories automatically on the basis of natural language processing. Specifically, the sentence patterns of each component in a user story are first analysed and determined such that the critical structure in the representative tasks can be automatically extracted based on the user story meta-model. The similarity of user stories is calculated, which can be used to generate the connected graph as the basis of automatic user story clustering. We evaluate the approach based on thirteen datasets, compared against ten baseline techniques. Experimental results show that our clustering approach has higher accuracy, recall rate and F1-score than these baselines. It is demonstrated that the proposed approach can significantly improve the efficacy of user story clustering and thus enhance the overall performance of agile development. The study also highlights promising research directions for more accurate requirements elicitation. [ABSTRACT FROM AUTHOR]

The traditional combat system-of-systems (SoS) has a long development process of capability requirements, a weak rapid response capabilities to the dynamic changes in the battlefield situation, single source of requirements and lack of feedback adjustment mechanism. In view of the above problems, the agile development method of combat SoS capability requirements based on system engineering "V" model and user stories is proposed. The development framework of combat SoS capability requirements is redesigned. The combat SoS capability requirements model is built based on the user stories. And a complete set of combat SoS capability requirements development process is proposed. Finally, the development process of combat SoS capability requirements is demonstrated with the example of "minefield cleaning" combat task. The example research results indicate that the proposed method provides a new technical path for solving the bidirectional source contradiction problem of combat SoS capability requirements, and can effectively improve the efficiency of developing combat SoS capability requirements. [ABSTRACT FROM AUTHOR]

Drug development in rare diseases is challenging due to the limited availability of subjects with the diseases and recruiting from a small patient population. The high cost and low success rate of clinical trials motivate deliberate analysis of existing clinical trials to understand status of clinical development of orphan drugs and discover new insight for new trial. In this project, we aim to develop a user centered Rare disease based Clinical Trial Knowledge Graph (RCTKG) to integrate publicly available clinical trial data with rare diseases from the Genetic and Rare Disease (GARD) program in a semantic and standardized form for public use. To better serve and represent the interests of rare disease users, user stories were defined for three types of users, patients, healthcare providers and informaticians, to guide the RCTKG design in supporting the GARD program at NCATS/NIH and the broad clinical/research community in rare diseases. [ABSTRACT FROM AUTHOR]

The Standish Group Reports 83.9% of IT Projects fail, and one of the top factors in failed projects is the incomplete requirements or user stories. Therefore, it is essential to teach undergraduate students from computer science degree programs how to create complete user stories. Computer science programs include some subjects or topics involving requirements or user stories collection and writing, such as Requirements Engineering, Software Engineering, Project Management, or Quality Software Assurance. For that reason, we designed a web application called User Story Quality Analyzer (USQA) that uses Natural Language Processing modules to detect errors regarding aspects of usefulness, completeness, and polysemes in the user stories creation. The tool was proved from three perspectives: (1) a reliability test, where 35 user stories developed by experts were tested in the app to prove the prototype's reliability; (2) usability and utility analysis; 48 students interacted with the tool and responded a Satisfaction Usability Scale and an open‐ended question, the students reported a high usability score; (3) finally, error classification, we gathered 159 user stories processed by the system, and we classified the students' common errors considering incompleteness and polysemes. After the evaluations, we concluded that USQA could evaluate the user stories as an expert, which could help the professors/teachers/instructors in their courses by providing feedback to the students when they are writing user stories. [ABSTRACT FROM AUTHOR]

As various estimation methods are available to predict the completion time of requirements at earlier phase of software development. Function point analysis used to estimate the efforts required to develop the traditional project on time. Now industries are moving to the agile technology to develop the project with involvement of customer. team member and tester as well. To advancement in the agile development the requirements are framed in the form of user stories. In today IT industries. essential view of agile is estimation methods are required to measure the user stories. Need of estimation method of agile through the implement of function point analysis for prediction. To improve the organization success. project completion needs to done on time. There is requirement of approach to find out the actual effort estimation of user story. In this paper. assignment of function point to user story is done and find out the effort estimation required to implement the user stories on the basis of different complexity weights. Initially. extract the "I want" section and obtain the number of transaction types (external inputs. external outputs and external inquires) on the basis of operational keywords appearance This proposed work computed UFR TDL VAR adjusted FP and efforts in hours on the basis of weights such as low, average. and high of IFPUG and the average by release 5 of ISBSG. The average complexity weight of the IFPUG and the average of release 5 of the 1SBSG are the key parameters for the comparison perspective. The outcome of the proposed work provides the adjusted FP and effort estimation in hours. In order to predict effort required. the interpolated values of effort estimation is calculated using Lagrange's interpolation. Both outcomes give the usage of FPA through IFPUG and ISBSG to estimate the effort so that the completion of project on time. Function point analysis on agile user story is used to improve the organization success. [ABSTRACT FROM AUTHOR]

A user story is an artifact in software development processes that can be used for automated test cases. This study proposes a terminal-based program for converting user stories into source codes for automated testing. We also propose a modified user story format that can be directly converted into functional Python-based Selenium source codes. We then implement the program by creating user stories for several local Django projects and using the converted codes. The implementation results show that the code from the program has a reliability rate of 72% for successfully executing it as the users' intentions. [ABSTRACT FROM AUTHOR]

Nowadays, most software companies have adopted agile development methodologies, which suggest the capture of requirements through user stories. Issues Management Systems allow development teams to manage user stories and other issues, such as errors, change requests, and others. Although these systems provide features for categorizing or labeling issue types, the user often needs to include or specify this information correctly. A poor issue categorization causes many user stories to end up buried in a large volume of data, making it difficult to identify them. This article presents and compares three neural network models to classify issues as User Stories. As the ultimate goal of this research is to improve the quality of the software development project documentation, the comparison is practical to select a model to be embedded in an IMS tool for automatically categorizing issues. The compared models are a BRNN-LSTM model, an Elmo-based model, and a BERT-based model. It applied the CRISP-MD methodology to train, validate, and test the three proposed neural network models. Then, a comparison was performed regarding their accuracy and performance. As a result, the article shows that the BERTbased model is the one that best fits the problem posed, managing to classify the issues as user stories with an accuracy of approximately 97%. This model can analyze the text syntactically and semantically with the best accuracy and performance. [ABSTRACT FROM AUTHOR]

At an early stage in the development process, a development team must obtain insight into the software being developed to establish a reliable plan. Thus, the team members should investigate, in depth, any information relating to the development. A major challenge for developers is software development effort estimation (SDEE), which refers to gauging the amount of effort needed to develop the software. In agile methodologies, a project is delivered in iterations, each of which delivers a set of requirements known as user stories. Therefore, SDEE in agile focuses on estimating a single user story's effort, not the project as a whole, as in traditional development. Among the various techniques, data-driven methods have proved effective in effort estimation, as they are unaffected by external pressure from managers. Moreover, no experts have to be available at the point when estimation is undertaken. By conducting a systematic literature review, this study presents a comprehensive overview of data-driven techniques for user story effort estimation. The results show that there has been limited work on this topic. Studies were analysed to address questions covering five main points: technique; performance evaluation method; accuracy, independent factors (effort drivers); and the characteristics of the datasets. The main performance evaluation methods are performance measures, baseline benchmarks, statistical tests, distribution of estimates, comparison against similar existing techniques and human estimation. Four types of independent factors were identified: personnel; product; process; and estimation. Furthermore, the story point was found to be the most frequently used effort metric in agile user stories. [ABSTRACT FROM AUTHOR]

Agile is often associated with a lack of architectural thinking causing technical debt but has the advantage of user centricity and a strong focus on value. Model-driven software engineering (MDSE) strongly performs for building a quality architecture and code, but lacks focus on user requirements and tends to consider development as a monolithic whole. The combination of Agile and MDSE has been explored, but a convincing integrated method has not been proposed yet. This paper addresses this gap by exploring the specific combination of MERODE—as an example of a proven MDSE method—with Scrum, a reference agile method offering a concrete (sprint-based) life cycle management on the basis of user stories. The method resulting of this integration is called Agile MERODE; it is driven by user stories, themselves associated with behavior-driven development scenarios. It allows for domain-driven design and permits fast development from domain models by means of code generation. An illustrative example further clarifies the practical application of Agile MERODE, while a case study shows the planning game application in the case's context. While the approach, in its entirety, allows reducing technical debt by building the architecture in a logical, consistent and complete manner, introducing MDSE involves a trade-off with pure value-driven development. Agile MERODE contributes to the state of the art by showing how to increase user centricity in MDSE, how to align model-driven engineering with the Scrum cycle, and how to reduce the technical debt of agile developments yet remaining value-focused. [ABSTRACT FROM AUTHOR]

Effort estimation based on user stories plays a pivotal role in agile software development, where accurate predictions of project efforts are vital for success. While various supervised ML tools attempt to estimate effort, the prevalence of estimation errors presents significant challenges, as evidenced by the CHAOS report by the Standish Group, which highlights incorrect estimations contributing to a substantial percentage of failed agile projects. This research delves into the domain of user story-based effort estimation in agile software development, aiming to explore the issues arising from inaccurate estimations. The primary goal is to uncover these issues comprehensively and propose potential solutions, thus enhancing the efficacy of the user story-based estimation method. To achieve the research objectives, a systematic literature review (SLR) is conducted, surveying a wide range of sources to gather insights into issues surrounding user story-based effort estimation. The review encompasses diverse estimation methods, user story attributes, and the array of challenges that can result from inaccurate estimations. The SLR reveals a spectrum of issues undermining the accuracy of user story-based effort estimation. It identifies internal factors like communication, team expertise, and composition as crucial determinants of estimation reliability. Consistency in user stories, technical complexities, and task engineering practices also emerge as significant contributors to estimation inaccuracies. The study underscores the interconnectedness of these issues, emphasizing the need for a standardized protocol to minimize inaccuracies and enhance estimation precision. In light of the findings, it becomes evident that addressing the multi-dimensional factors influencing user story-based effort estimation is imperative for successful agile software development. The study underscores the interplay of various aspects, such as team dynamics, task complexity, and requirement engineering, in achieving accurate estimations. By recognizing these challenges and implementing recommended solutions, software development processes can avoid failures and enhance their prospects of success in the agile paradigm. [ABSTRACT FROM AUTHOR]

A common way to describe requirements in Agile software development is through user stories, which are short descriptions of desired functionality. Nevertheless, there are no widely accepted quantitative metrics to evaluate user stories. We propose a novel metric to evaluate user stories called instability, which measures the number of changes made to a user story after it was assigned to a developer to be implemented in the near future. A user story with a high instability score suggests that it was not detailed and coherent enough to be implemented. The instability of a user story can be automatically extracted from industry-standard issue tracking systems such as Jira by performing retrospective analysis over user stories that were fully implemented. We propose a method for creating prediction models that can identify user stories that will have high instability even before they have been assigned to a developer. Our method works by applying a machine learning algorithm on implemented user stories, considering only features that are available before a user story is assigned to a developer. We evaluate our prediction models on several open-source projects and one commercial project and show that they outperform baseline prediction models. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

Context: 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. Objective: Conduct a systematic literature review to capture the current state-of-the-art of NLP research on user stories. Method: 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. Results: 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. Conclusion: 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.

Data science and machine learning are buzzwords of the early 21st century. Now pervasive through human civilization, how do these concepts translate to use by researchers and clinicians in the life-science and medical field? Here, we describe a software toolkit, just large enough in scale, so that it can be maintained and extended by a small team, optimised for problems that arise in small/medium laboratories. In particular, this system may be managed from data ingestion statistics preparation predictions by a single person. At the system's core is a graph type database, so that it is flexible in terms of irregular, constantly changing data types, as such data types are common during explorative research. At the system's outermost shell, the concept of 'user stories' is introduced to help the end-user researchers perform various tasks separated by their expertise: these range from simple data input, data curation, statistics, and finally to predictions via machine learning algorithms. We compiled a sizable list of already existing, modular Python platform libraries usable for data analysis that may be used as a reference in the field and may be incorporated into this software. We also provide an insight into basic concepts, such as labelled-unlabelled data, supervised vs. unsupervised learning, regression vs. classification, evaluation by different error metrics, and an advanced concept of cross-validation. Finally, we show some examples from our laboratory using our blood sample and blood clot data from thrombosis patients (sufferers from stroke, heart and peripheral thrombosis disease) and how such tools can help to set up realistic expectations and show caveats. [ABSTRACT FROM AUTHOR]

Software requirements query is the first stage in software requirements engineering. Elicitation is the process of identifying software requirements from various sources such as interviews with resource persons, questionnaires, document analysis, etc. The user story is easy to adapt according to changing system requirements. The user story is a semi-structured language because the compilation of user stories must follow the syntax as a standard for writing features in agile software development methods. In addition, user story also easily understood by end-users who do not have an information technology background because they contain descriptions of system requirements in natural language. In making user stories, there are three aspects, namely the who aspect (actor), what aspect (activity), and the why aspect (reason). This study proposes the extraction of user stories consisting of who and what aspects of online news sites using feature extraction and maximum entropy as a classification method. The systems analyst can use the actual information related to the lessons obtained in the online news to get the required software requirements. The expected result of the extraction method in this research is to produce user stories relevant to the software requirements to assist systems analysts in generating requirements. This proposed method shows that the average precision and recall are 98.21% and 95.16% for the who aspect; 87,14% and 87,50% for what aspects; 81.21% and 78.60% for user stories. Thus, this result suggests that the proposed method generates user stories relevant to functional software. [ABSTRACT FROM AUTHOR]

In modern software development processes, software effort estimation plays a crucial role. The success or failure of projects depends greatly on the accuracy of effort estimation and schedule results. Many studies focused on proposing novel models to enhance the accuracy of predicted results; however, the question of accurate estimation of effort has been a challenging issue with regards to researchers and practitioners, especially when it comes to projects using agile methodologies. This study aims at introducing a novel formula based on team velocity and story point factors. The parameters of this formula are then optimized by employing swarm optimization algorithms. We also propose an improved algorithm combining the advantages of the artificial bee colony and particle swarm optimization algorithms. The experimental results indicated that our approaches outperformed methods in other studies in terms of the accuracy of predicted results.