User story

About: User story is a research topic. Over the lifetime, 1078 publications have been published within this topic receiving 23717 citations.

Lazy User Theory: A Dynamic Model to Understand User Selection of Products and Services

Abstract: In this paper we suggest that a user will most often choose the solution that will fulfill her (information) needs with the least effort. We call this “lazy user behavior”. We suggest that the principle components responsible for solution selection are the user need and the user state. User need is the user’s detailed (information) need (urgency, type, depth, etc.) and user state is the situation, in which the user is at the moment of the need (location, time, etc.); the user state limits the set of available solutions (devices) to fulfill the user need. We present the lazy user theory of solution selection, two mBusiness case examples, and discuss the implications of lazy user behavior on user selection of products and services. Implications on the design of new products and services are also discussed.

Intelligent adaptive navigation optimization

Abstract: A method and apparatus for optimizing the navigation of lists or other hierarchies of alternatives, as presented to the user by electronic devices and computer networks, by automatically recommending the alternatives of the next list to be presented. Each alternative is recommended on the basis of the importance of that alternative to the user, or to the operator of the service provided to the user, or to providers of applications that can be selected by the user. The optimization is based upon probabilities estimated by mathematical functions on several variables, statistics, and parameters, including but not limited to the user location, time and date, user's expressed personal preferences, service operators' and application providers' expressed commercial intentions, service operators' and application providers' expressed business rules, implied application relationships, personal information of the user, usage statistics of the user, usage statistics of the general public, and embedded parameters which refine a statistical model of the user's behavior. Such optimization reduces the efforts required of a user to select one item, out of a structure of lists of items, by reorganizing the structure so that the user is likely to use fewer keystrokes or spoken words to select an item of combined higher importance to the user, the service operator, and the application provider. The optimization is personalized to each user by presenting the lists and structures to each user in a way that is automatically adapted to the statistical and deterministic factors pertaining to that individual user, so that users of different personalities and preferences can see or hear differently organized lists from which to choose. The number of keystrokes, or spoken words, is further reduced by intelligent data sharing between applications to avoid requiring the user's reentry of the same data to one application that was already entered in another.

Detecting user story information in developer-client conversations to generate extractive summaries

Abstract: User stories are descriptions of functionality that a software user needs They play an important role in determining which software requirements and bug fixes should be handled and in what order Developers elicit user stories through meetings with customers But user story elicitation is complex, and involves many passes to accommodate shifting and unclear customer needs The result is that developers must take detailed notes during meetings or risk missing important information Ideally, developers would be freed of the need to take notes themselves, and instead speak naturally with their customers This paper is a step towards that ideal We present a technique for automatically extracting information relevant to user stories from recorded conversations between customers and developers We perform a qualitative study to demonstrate that user story information exists in these conversations in a sufficient quantity to extract automatically From this, we found that roughly 102% of these conversations contained user story information Then, we test our technique in a quantitative study to determine the degree to which our technique can extract user story information In our experiment, our process obtained about 708% precision and 183% recall on the information

The Use of Explicit User Models in a Generation System for Tailoring Answers to the User’s Level of Expertise

Abstract: A question-answering program providing access to a large amount of data will be most useful if it can tailor its answers to each individual user. In particular, a user’s level of knowledge about the domain of discourse is an important factor in this tailoring if the answer provided is to be both informative and understandable to the user. In this research, we address the issue of how the user’s domain knowledge can affect an answer. By studying texts, we found that the user’s level of domain knowledge affected the kind of information provided and not just the amount of information, as was previously assumed. Depending on the user’s assumed domain knowledge, a description can be either parts-oriented or process-oriented. Thus the user’s level of expertise in a domain can guide a system in choosing the appropriate facts from the knowledge base to include in an answer. We propose two distinct descriptive strategies that can be used in a question-answering program, and show how they can be mixed to include the appropriate information from the knowledge base, given the user’s domain knowledge. We have implemented these strategies in TAILOR, a computer system that generates descriptions of devices. TAILOR uses one of the two discourse strategies identified in texts to construct a description for either a novice or an expert. It can merge the strategies automatically to produce a wide range of different descriptions to users who fall between the extremes of novice or expert, without requiring an a priori set of user stereotypes.

Case studies of end user requirements for interactive problem-solving systems

Abstract: Interactive problem-solving is defined as user/machine dialogues to identify and solve problems with imprecise solution criteria. Although high payoffs from interactive problem-solving systems have been predicted, few such systems are in use. A key problem is the lack of understanding of the requirements of the potential users. This paper presents the results of case studies of the use of an interactive problem-solving system. Based on observations from these case studies, a list of user characteristics have been compiled relating to user behavior (e.g., data user and problem solving methods) and user requirements (e.g., the need for involvement in the solution process).