Topic
User story
About: User story is a research topic. Over the lifetime, 1078 publications have been published within this topic receiving 23717 citations.
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15 Sep 2003
TL;DR: It is shown that agility is a result of a more effective knowledge creation process involving both the customer and development team, and generic knowledge management theories can be used to explain how XP practices improve an organization's agility.
Abstract: Extreme programming (XP) is an accepted and widely used agile software development method with active user communities worldwide. There exists a great deal of anecdotal evidence to support the use of XP in different situations. While some empirical evidence regarding the use of a certain composite of XP practices exist, no scientifically grounded theory exists. Based on the notion that software engineering is knowledge-intensive work, we show that generic knowledge management theories can be used to explain how XP practices improve an organization's agility. Tuomi's [I. Tuomi, (!999)] model for knowledge creation is used to explain the different practices of XP. This gives a new perspective to agile processes and enables others to express agile practices in a more generic way. It is shown that agility is a result of a more effective knowledge creation process involving both the customer and development team. Based on this, practical implications and future research needs are identified.
27 citations
TL;DR: Results show that groups with a lower productivity in writing user stories and less professional experience in managing software requirements present a non-collaborative behavior more frequently, and that teams with a fewer number of interventions are more likely to produce a greater number of user stories.
Abstract: Software engineering is the application of principles used in engineering design, development, testing, deployment, and management of software systems. One of the software engineering’s approaches, highly used in new industries, is agile development. User stories are a commonly used notation to capture user requirements in agile development. Nevertheless, for the elaboration of user stories, a high level of collaboration with the client is necessary. This professional skill is rarely measured or evaluated in educational contexts. The present work approaches collaboration in software engineering students through multimodal learning analytics, modeling, and evaluating students’ collaboration while they are writing user stories. For that, we used multidirectional microphones in order to derive social network analysis metrics related to collaboration (permanence and prompting) together with human-annotated information (quality of the stories and productivity). Results show that groups with a lower productivity in writing user stories and less professional experience in managing software requirements present a non-collaborative behavior more frequently, and that teams with a fewer number of interventions are more likely to produce a greater number of user stories. Moreover, although low experience subjects produced more user stories, a greater productivity of the most experienced subjects was not statistically verified. We believe that these types of initiatives will allow the measurement and early development of such skills in university students.
27 citations
Book•
01 Jan 2004
TL;DR: The Third International Workshop on Empirical Evaluation of Agile Methods ("The Data Workshop") as mentioned in this paper was the first workshop to evaluate the applicability of the Agile CMM.
Abstract: Papers- Combining Formal Specifications with Test Driven Development- Long Build Trouble Shooting Guide- Acceptance Testing vs Unit Testing: A Developer's Perspective- The Role of Process Measurement in Test-Driven Development- Acceptance Test Driven Planning- An Agile Customer-Centered Method: Rapid Contextual Design- Suitability of FIT User Acceptance Tests for Specifying Functional Requirements: Developer Perspective- Using Storyotypes to Split Bloated XP Stories- Distributed Pair Programming: An Empirical Study- Support for Distributed Pair Programming in the Transparent Video Facetop- Toward a Conceptual Framework of Agile Methods- Security Engineering and eXtreme Programming: An Impossible Marriage?- An Agile CMM- Adapting Extreme Programming to Research, Development and Production Environments- Outsourcing and Offshoring with Agility: A Case Study- User Story Methodology Adaptations for Projects Non-traditional in Scope and Customer GUI Contributions- Agile CS1 Labs: eXtreme Programming Practices in an Introductory Programming Course- A Case Study in the Use of Extreme Programming in an Academic Environment- Workshops: Research Close to the Action- Who Should Write Acceptance Tests?- Getting Leaders On-Board- Third International Workshop on Empirical Evaluation of Agile Methods ("The Data Workshop")- How to Maintain and Promote Healthy Agile Culture- UI Design as Part of an Agile Process- Agile Development for Embedded Software- Refactoring Our Writings- Agile Tests as Documentation- Fit Fest- Panels- Agile Project Management- Agile Methods for Safety-Critical Software Development- Is XP Still Relevant?- Tutorials- to Tutorials- Agile Requirements: Tailoring the Functional Requirements Specification Process to Improve Agility- Advanced Fit Lab- Effective User Stories- Outsourcing and Offshoring with Agility- Traditional and Agile Project Management: A Practical Mapping- The Agile/XP Team Primer: Exploring Self-organizing Teams- Coaching Agile Software Teams- Getting the Software You Need: A Practical Approach for Testers and the Customer Team- First Encounter with Agile Methods- Working Effectively with Legacy Code- The Art of Acceptance Testing- Agile Planning, Tracking, and Project Management Boot Camp- Tutorial: Agile Project Management - Reliable Innovation- XP for a Day- Scripting Web Tests- Interaction Design Meets Agility: Practicing Usage Centered Design on Agile Development Projects- Agile Implementations, Agile Impediments, and Agile Management- The Lean Maturity Measure Assessment and Implementation- Agile Databases- Transitioning to XP- Large Scale Agile Software Development- Refactoring to Patterns
27 citations
01 Dec 2020
TL;DR: ABCDE as discussed by the authors is based on Scrum and is therefore iterative and incremental, where the requirement gathering with user stories, the iterative-incremental approach, the key roles, and the meetings.
Abstract: Blockchain software development is becoming more and more important for any modern software developer and IT startup. Nonetheless, blockchain software production still lacks a disciplined, organized and mature development process, as demonstrated by the many and (in)famous failures and frauds occurred in recent years. In this paper we present ABCDE, a complete method addressing blockchain software development. The method considers the software integration among the blockchain components—smart contracts, libraries, data structures—and the out-of-chain components, such as web or mobile applications, which all together constitute a complete DApp system. We advocate for ABCDE the use of agile practices, because these are suited to develop systems whose requirements are not completely understood since the beginning, or tend to change, as it is the case of most blockchain-based applications. ABCDE is based on Scrum, and is therefore iterative and incremental. From Scrum, we kept the requirement gathering with user stories, the iterative-incremental approach, the key roles, and the meetings. The main difference with Scrum is the separation of development activities in two flows—one for smart contracts and the other for out-of-chain software interacting with the blockchain—each performed iteratively, with integration activities every 2–3 iterations. ABCDE makes explicit the activities that must be performed to design, develop, test and integrate smart contracts and out-of-chain software, and documents the smart contracts using formal diagrams to help development, security assessment, and maintenance. A diagram derived from UML class diagram helps to effectively model the data structure of smart contracts, whereas the exchange of messages between the entities of the system is modeled using a modified UML sequence diagram. The proposed method has also specific activities for security assessment and gas optimization, through systematic use of patterns and checklists. ABCDE focuses on Ethereum blockchain and its Solidity language, but preserves generality and with proper modifications might be applied to any blockchain software project. ABCDE method is described in detail, and an example is given to show how to concretely implement the various development steps.
27 citations
01 Jan 2020
TL;DR: Through intensive and literature review, it can be inferred that machine learning models clearly outperformed non-machine learning and traditional techniques of estimation in estimation of software projects.
Abstract: It is inevitable for any successful IT industry not to estimate the effort, cost, and duration of their projects. As evident by Standish group chaos manifesto that approx. 43% of the projects are often delivered late and entered crises because of overbudget and less required functions. Improper and inaccurate estimation of software projects leads to a failure, and therefore it must be considered in true letter and spirit. When Agile principle-based process models (e.g., Scrum) came into the market, a significant change can be seen. This change in culture proves to be a boon for strengthening the collaboration between developer and customer. Estimation has always been challenging in Agile as requirements are volatile. This encourages researchers to work on effort estimation. There are many reasons for the gap between estimated and actual effort, viz., project, people, and resistance factors, wrong use of cost drivers, ignorance of regression testing effort, understandability of user story size and its associated complexity, etc. This paper reviewed the work of numerous authors and potential researchers working on bridging the gap of actual and estimated effort. Through intensive and literature review, it can be inferred that machine learning models clearly outperformed non-machine learning and traditional techniques of estimation.
27 citations