=Paper=
{{Paper
|id=Vol-2983/iStar21_paper_6
|storemode=property
|title=A Preliminary Framework for Constructing iStar Models from User Stories
|pdfUrl=https://ceur-ws.org/Vol-2983/iStar21_paper_6.pdf
|volume=Vol-2983
|authors=Chunhui Wang,Chao Wu,Tong Li,Zhiguo Liu
|dblpUrl=https://dblp.org/rec/conf/istar/WangWLL21
}}
==A Preliminary Framework for Constructing iStar Models from User Stories==
https://ceur-ws.org/Vol-2983/iStar21_paper_6.pdf
A Preliminary Framework for Constructing iStar
Models from User Stories
Chunhui Wang1 , Chao Wu1 , Tong Li2 and Zhiguo Liu1
1
College of Computer Science and Technology, Inner Mongolia Normal University, 81 Zhaowuda Road, Hohhot 010022,
China
2
Faculty of Information Technology, Beijing University of Technology, 100 Ping Le Yuan, Beijing 100124, China
Abstract
User stories have been increasingly adopted in practice due to their intuitive structure, which specify
users’ needs for the software system from their perspectives. As iStar modeling framework also em-
phasizes user perspectives, some of its concepts and relationships can potentially be aligned with user
stories. In this paper, we propose to (semi-) automatically derive iStar models based on user stories, with
the aim of facilitating iStar modeling and promoting the practical adoption of iStar models. Specifically,
this paper focuses on investigating an appropriate way of balancing the manual and automatic analysis
during the modeling process, based on which we present a visionary framework.
Keywords
iStar, User story, Model construction
1. Introduction
User story is a widely adopted requirements notation in agile development. Generally, user
stories are written by customers or users in natural language with limited format. For example,
Cohn suggests a user story pattern[1]: As a , I want , so that
. Although a user story is short and simple, it describes a feature told from the
perspective of the users/customers who desires the capability of the software system.
The iStar modeling technique is one of the most relevant goal-oriented requirements engi-
neering approaches and provides a view of involved actors and their dependencies[2]. iStar
allows for the clear and explicit dependencies of goals to facilitate understanding of stakeholder
needs, dependencies, etc. Through investigating the patterns of user stories, we argue that
some of iStar concepts and relationships can potentially be aligned. For example, actor concept
can be corresponded to type of user field, task or goal concept can be found from some intention
and some reason fields, and relationships can be identified from the semantics of hierarchy and
interdependence between user stories.
However, since user stories are short, simple descriptions and are generally presented in a
flat list, it is difficult to identify iStar concepts and relationships from user stories. Existing
research works have used requirements modeling methods, such as Rationale Tree[3] and Goal
Proceedings of the 14th International iStar Workshop, October 18-21, 2021, St. Johns (NL), Canada
EMAIL: ciecwch@imnu.edu.cn (C. Wang); wuchao@mails.imnu.edu.cn (C. Wu); litong@bjut.edu.cn (T. Li);
cieclzg@imnu.edu.cn (Z. Liu)
© 2021 Copyright for this paper by its authors.
CEUR Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
http://ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
35
Net[4], to model goals. These works use a graphical representation of user story sets to identify
the hierarchy of goal concepts and their interdependence. In addition, because user stories are
based on nature language expressions, some studies have used heuristic rules to automated
goal model extraction using natural language processing[5]. However, due to the ambiguity
of natural language descriptions, the precision results were still not as expected[6]. To ensure
obtain practical adoption models from user stories, first, user stories should follow good writing
specifications, and secondly, the nodes and edges that are automatically identified from use
stories need to be confirmed manually. A corresponding research objective is how to maximally
reduce such manual efforts and produce practically useful results.
This paper presents a preliminary framework for semi-automatically constructing iStar
models from user stories. It is a human-assisted process, in which the concepts and relationships
are first automatically extracted from user stories and then reviewed by analysts to generate
iStar models. Specifically, this paper focuses on investigating the mapping between user stories
and iStar models and providing an appropriate way of balancing the manual and automatic
analysis during the modeling process, based on which we present a visionary framework.
2. Related Work
2.1. User Story
Follow the pattern of user stories, a user story contains three dimensions: Who, What and
Why. In particular, in a user story (which is described with the way: As a , I
want , so that ), reflects the actor who uses the
system, usually reflects what function/non-function the actor provides or uses, or
what task/goal wants to achieve, and usually reflects why the actor has the
and indicates a goal or quality to achieve after the intention is finished.
User stories are subject to a process of sorting and dropping early on the development
project[7]. From a macro-level perspective, there is a hierarchical relationship between user
stories, that is, a user story may be an epic story, and the achievement of this story requires
the completion of some fine-grained stories. In addition, there may be a temporal relationship
between two user stories at the same grain, i.e., the realization of one user story depends on the
other story. Thus, user stories can be grouped for serving for project composition/decomposition.
2.2. NLP for generating model from user stories
Natural language processing (NLP) for generating software model from user stories has become
popular with the increase of agile software development, such as generating goal models[5],
conceptual models[8] or UML models[9] from user stories. The main approaches commonly
used were preprocessing, part-of-speech tagging (POS tag), syntactic parse tree and semantic
analysis[6]. Preprocessing was used for preparing the data and usually included tokenization,
filtering, and stop-word removal. POS tag was used to identify verbs and nouns as elements in
models. Syntactic parse tree was normally used to represent a lexical category of a sentence
and know grammatical relationships in the sentence. Several methods were used to obtain and
understand the semantic connections in user stories, such as cosine similarity function and
36
clustering for semantic similarity. Some methods were used to identify topics inside user stories
for heuristic analysis using LDA, Word Vectors and word embeddings. In our work, we will plan
using NLP to perform lexical, syntax and semantic analysis of user stories, and automatically
identify concepts and relationships to facilitate iStar model generation.
3. Proposal
The proposed framework for constructing iStar models from user stories comprises four func-
tional modules: quality improvement, node identification, edge identification and model gener-
ation. Figure 1 shows the process modules and data flows within the framework. The function
of each data processing module is described as follows:
1. Quality Improvement: automatically checks for irregularities in user stories, and promotes
stakeholders to provide high-quality user stories.
2. Node identification: automatically identifies the concepts of iStar model from user stories.
3. Edge identification: automatically identifies the relationships between the identified
concepts from user stories.
4. Model generation: requirements engineers or agile teams review the identified concepts
and relationships and interactively generate iStar models.
Legend
1 Data flow
User stories Quality
Improvement data process Interactive flow
stakeholders 2
Node Concepts
Identification
Requirements engineers
3
Edge Relationships
Identification
Model iStar Models
4
Generation
Figure 1: The process framework of constructing iStar models from user stories
The framework shown in Figure 1 includes two iterative interactive activities. The first is
that stakeholders modify user stories iteratively to ensure writing good user stories[10] with
good readability and understandability. The second is that requirements engineers refer to
the identified concepts and relationships and construct iStar model iteratively to ensure the
precision of the model results.
In this work, a iStar model consists of a set of nodes that represent iStar model con-
cepts and a set of edges that represent relationships among nodes, expressing that an actor
37
wants/needs/would like to achieve an intentional element. The intentional element can be a
resource, task, goal and quality. The details of the above concepts and relationships see [11].
3.1. Quality improvement
Although the user story has simple structure, there are often poorly written in practice and
exhibit inherent quality defects[12]. We have carried out research work on improving the
quality of user stories. In [13], we proposed 11 quality criteria to evaluate the quality defects
of incomplete, inconsistent and untestable in user stories, and an approach based on model
driven and NLP is proposed for finding the user stories with quality defects. In this work, these
techniques in [13] will be used to ensure that users can write good stories.
3.2. Node identification
Nodes of iStar model can be aligned with the fields of who, what, why in user stories. In this
work, user story can be written with the following grammar:
• As a , I want/want to/need/can/would like ///,
[so that // ]
To improve the understandability of the transition from user story to the iStar nodes, the
description (actor/task/capability/goal/quality) shall be kept short and precise. We advice these
descriptions use the following syntax [14]:
• Actor: [Adjective]+ Noun;
• Task: Verb+Object+[Complement];
• Resource/Quality: [Adjective]+Object;
• Goal: Object + Passive_Verb + [Complement];
The node identification process mainly includes three steps. The first step is to identify the
fields of who, what and why. The second step is to analyze the syntactic components of each
field by preprocessing to get a set of sentences. The third is to obtain the concepts based on
the result of matching syntactic components of the sentence in user story and the intentional
element. In the third step, POS tag and syntactic parse tree are used in order to know the
composition of the sentence.
3.3. Edge identification
According to [11], in this work, the edges in iStar mainly includes three types: actors association
links, social dependencies, and intentional element links. To identify these edges, we provide
corresponding heuristic rules and detection methods.
38
3.3.1. Actors association links
Actor links are binary, linking a single actor to a single other actor. Two types of actor links
exist: is-a and part-of. To identified these actor links, we plan to adopt the method of actor
modeling[1], that is, extract the actors/roles in all user stories, and then aggregate these actors
according to their similarities, and recognize the links with the help of humans. In this phase,
we plan to use the N-gram and WordNet to calculate the similarity among actors.
3.3.2. Social dependencies
Social dependencies are one-way, linking a dependerElmt within the depender actor to the
dependum (an intentional element), outside actor boundaries, to the dependerElmt within the
dependee actor. To identify the social dependencies, we plan to use the following two heuristics.
Heuristic Rule I: For a set of user stories, an object-based similarity calculation method is
used to identify the temporal relationship between user stories[13], that is, if two user stories
discuss similar objects, there may be social dependence between them, and the temporal order
implies the direction between them.
Heuristic Rule II: For the two user stories 𝑢1 and 𝑢2 that have a temporal relationship, POS
tag and syntactic parse tree are used to identify the corresponding concepts, and then the social
dependency relationship is fed back. Among them, one of the description format of 𝑢1 and 𝑢2
is as follows:
• 𝑢1 : As a , I want/want to/need/can/would like , [so that
/]
• 𝑢2 : As a , I want/want to/need/can/would like , so that
In some real case, some elements (dependerElmt and dependeeElmt) in the social dependency
relationship may be omitted, and heuristic rules will be formulated for different omissions in
our follow-up work.
3.3.3. Intentional element links
There are four types of links between intentional elements[11]: neededBy, refinement, contribute
and qualification. We define the following rules and methods to identify these links.
Heuristic Rule III: For a user story pattern, As an , I want so that ,
there is a neededBy link between a task and a resource.
To identify the neededBy link, POS tag and syntactic parse tree are used to find the concepts
of resource and task and above the user story pattern.
Heuristic Rule IV: When there are hierarchical relationships between a goal (task) and
some sub-goals (sub-tasks), there are refinement links between the goal (task) and the sub-goals
(sub-tasks).
To discover the hierarchical relationships between goals/tasks, we plan to use a classification
method based on themes which are extracted from user stories. This process mainly includes
39
two key steps. One is to extract terms from a set of goals (or tasks). The other is to classify
goals/tasks using a text mining method based on topic modeling.
Heuristic Rule V: For a user story pattern, As an , I want
so that , there is a qualification link between an intentional element and a quality.
To the qualification links, we further recognize the contribution link, i.e., manually analyze
the relationships from a source intentional element to a target quality and marked their types
of make, help, hurt, and break[11] according to their semantics.
3.4. Model generation
Model generation is a human-assisted process in which requirements engineers view and
modify the concepts and relationships identified by automated methods. We plan to design
and implement a human-computer interaction environment. The environment will support
the following functions: user story editor, user story quality improvement, iStar model con-
cept and relationship recommendation, recommended information manual editor, and model
visualization, etc.
The model generation process based on above human-computer interaction environment
consists of four steps. The first step is stakeholders write user stories in user story editor. At the
second step, the user story quality improvement module checks for and reports irregular and
incomplete writing in user stories. Then, stakeholders will modify their user stories according
to the feedbacks. The third step is iStar components in user stories are automated identified by
the iStar model concept and relationship recommendation module and a preliminary iStar model
is built. At the last step, the requirements engineers will review and modify the preliminary
iStar model using text or graphic editing functions.
4. Conclusions
This paper presented a preliminary framework for constructing iStar models from user stories.
The proposed framework consists of four modules, namely, quality improvement, node identifi-
cation, edge identification and model generation. Quality improvement is used to find irregular
user stories and improve the effect of automatically identifying concepts and relationships of
iStar models. The identification process considers the potential mappings between the user
stories and the model semantics. The model generation process is a human-assisted process
based on manual and the automated identification results, which reduces the workload of
manual modeling and promotes the practical adoption of iStar models. As for our next step
work, we plan to develop a prototype tool that implements our proposed framework. In addition,
we aim to conduct case studies to verify the effectiveness of the proposed approach.
Acknowledgments
This work is supported by the Project of Beijing Municipal Education Commission
(No.KM202110005025) and the Natural Science of Foundation of Inner Mongolia Province
(No.2021MS06024).
40
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