Analyzing User Story Quality: A Systematic Review of Common Issues

and Solutions

ao Vitor Oliveira

and Lisandra Manzoni Fontoura

Programa de P

os-Graduac¸

ao em Ci

encia da Computac¸

ao, Federal University of Santa Maria (UFSM), Brazil

Keywords:

User Stories, Issues, Solutions, Systematic Literature Review.

Abstract:

User stories are widely adopted in agile development, serving as a fundamental technique for capturing and

communicating software requirements. This paper aims to conduct a systematic literature review (SLR) to

identify and analyze studies that address issues found in user stories, as well as possible ways to solve them.

The primary motivation for this study was the advancement in the use of Large Language Models (LLMs),

particularly after the launch of ChatGPT in 2022 by OpenAI. The research identiﬁed the main issues and so-

lutions related to user stories between 2020 and 2024, focusing on issues related to user story quality. The

results indicate that the most common issue in user stories is quality problems, cited in 15 articles, followed

by requirements management and task assignment (12) and the derivation and generation of the conceptual

model (8). Estimation is the least mentioned issue, appearing only three times. Regarding solution methods,

researchers most frequently used Natural Language Processing, Machine Learning, and other Artiﬁcial Intelli-

gence techniques, citing them in 15 articles. This demonstrates the well-established application of AI methods

to address these challenges.

1 INTRODUCTION

User stories are widely adopted in agile development

as an essential technique for capturing and communi-

cating software requirements. Users or clients typi-

cally write these stories to describe their needs for the

future software system, using natural language and a

limited format. This method has become more pop-

ular due to its simplicity and ability to represent the

user’s perspective in a direct and comprehensible way

(Wang et al., 2022).

Approximately 70% of the professionals follow

the standardized format: “As a (persona), I want (ac-

tion) so that (beneﬁt),” which aids communication be-

tween stakeholders and development teams (Dalpiaz

and Brinkkemper, 2021).

However, despite their widespread adoption, user

stories often face challenges regarding clarity, com-

pleteness, and accuracy, which can undermine agile

development effectiveness and result in software that

only partially meets user needs (Wu et al., 2022). This

paper conducts a systematic literature review (SLR) to

identify and analyze these issues and explore oppor-

https://orcid.org/0009-0008-3582-5666

https://orcid.org/0000-0002-4669-1383

tunities for improving the use of user stories in agile

development.

The remainder of this paper is organized as fol-

lows. Section 2 presents the protocol used to conduct

the SLR. Section 3 describes and discusses the results,

and Section 4 concludes the paper.

2 REVIEW PROTOCOL

We conducted an SLR to identify the main issues

in speciﬁcations using user stories (US) and possi-

ble solutions. SLR is a research methodology that

seeks to rigorously synthesize and analyze all avail-

able evidence on a given topic of interest. It is widely

used in various ﬁelds, including software engineering

(Kitchenham and Charters, 2007).

The SLR followed the protocol deﬁned by

Kitchenham and Charters (2007), which consists of

three main phases: planning, conducting, and results

reporting. Each phase contains suggested methods

and procedures that must be followed to ensure that

the study provides acceptable and signiﬁcant results.

Figure 1 provides a summarized view of the process

carried out in the SLR.

152

Oliveira, J. V. and Fontoura, L. M.

Analyzing User Story Quality: A Systematic Review of Common Issues and Solutions.

DOI: 10.5220/0013218300003929

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 27th International Conference on Enterprise Information Systems (ICEIS 2025) - Volume 2, pages 152-159

ISBN: 978-989-758-749-8; ISSN: 2184-4992

Figure 1: Phases of the process used in the SLR.

2.1 Planning

The SLR planning consisted of three main steps:

identifying the need for the review, deﬁning the re-

search questions, and developing a review protocol.

In the ﬁrst step, we highlighted gaps in the liter-

ature to justify the need for this study. In the second

step, we deﬁned two research questions to identify so-

lutions to these issues. In the third step, we created a

review protocol that outlined inclusion and exclusion

criteria, search methods, and analysis procedures.

The review was necessary due to documented

issues with user story clarity, completeness, and

accuracy in agile development, coupled with the

lack of comprehensive studies addressing these prob-

lems. Research questions guide researchers in focus-

ing their studies by addressing speciﬁc questions to

achieve their objectives. We have formulated two re-

search questions to explore in this work.

RQ01. What issues related to user story speciﬁcation

did the study aim to solve?

RQ02. What solutions to the issues related to user

story speciﬁcation are described in the study?

The inclusion criteria targeted articles published

between 2020 and 2024, available in relevant

databases, and written in English while excluding ar-

ticles not focused on user story speciﬁcation. The

review also considered the rise of AI methods after

2022, following the launch of ChatGPT by OpenAI,

but adjusted the date range to ensure enough studies

were included.

We used the following search string:

((“user story” OR “customer Story” OR “fea-

ture description” OR “feature Descriptions” OR “cus-

tomer stories” OR “user stories”) AND (“issues” OR

“defects” OR “difﬁcult” OR “errors” OR “failures”)

2.2 Conducting

We conducted this systematic review using the Par-

sifal tool, deﬁning inclusion and exclusion criteria

to select relevant studies. The search across four

databases—ACM, IEEE, Science Direct, and Sco-

pus—yielded 888 articles, and we screened them

based on the predeﬁned criteria.

We evaluated the articles using acceptance criteria

such as clarity in describing issues, relevance to the

research, and, optionally, a description of solutions.

After applying these criteria, we selected 40 articles

for ﬁnal analysis, which we considered the most rel-

evant for understanding user story challenges in agile

development. Table 1 summarizes the selected works.

Table 1: The total number of articles found in the SLR.

Databases Initial search Selected

ACM 329 4

IEE 101 6

Science Direct 386 11

Scopus 72 19

Total 888 40

3 RESULTS AND DISCUSSION

This section describes the main issues found in the

systematic review. The issues were grouped based on

the work of Kustiawan and Lim (2023) to facilitate

understanding and more detailed analysis.

3.1 Research Question 01

To answer the question “RQ01: What issues related to

user story speciﬁcation did the study aim to solve?”,

we classiﬁed the identiﬁed issues into four categories:

User Story Quality, covering ambiguity and incom-

plete requirements (Section 3.1.1); Derivation and

Generation of Conceptual Models, dealing with man-

ual creation and conversion to pseudocode (Section

3.1.2); Requirements Management and Task Assign-

ment, focused on managing requirements and legal

demands (Section 3.1.3); and Estimation, addressing

cost, time, and effort predictions (Section 3.1.4).

3.1.1 User Story Quality

User stories are essential for communicating require-

ments in agile projects, helping the development team

Analyzing User Story Quality: A Systematic Review of Common Issues and Solutions

153

understand what needs to be delivered. However, as

noted by Hallmann (2020), writing user stories can

lead to formal errors such as ambiguities, inconsisten-

cies, and lack of detail. These issues affect the quality

of the stories and hinder the creation of a shared men-

tal model among team members, increasing the risk of

misunderstandings and project failure. Additionally,

several studies suggest non-textual alternatives to ad-

dress issues like unclear requirements and ambiguous

user stories.

Furthermore, insufﬁcient stakeholder involvement

increases ambiguity, as unclear system descriptions

lead to greater uncertainty about its exact require-

ments. Since the deﬁnition of requirements is car-

ried out in an early stage of development, users are

often unsure of what they want, as requirements tend

to evolve through discussions and interactions among

stakeholders and the technical team. Additionally,

conventional elicitation methods are limited in terms

of stakeholder participation and involvement, leaving

room for more ambiguous and incomplete require-

ments (Da Silva and Savi

c, 2021), (Gralha et al.,

2022), (Bakare et al., 2023).

Gupta et al. (2022) pointed out that although the

agile method has advantages such as ﬂexibility and

fast deliveries, it faces challenges in managing re-

quirements, particularly in ensuring clear and accu-

rate communication between stakeholders and devel-

opers. The lack of formal and detailed documentation

in agile processes can lead to ambiguities, rework, and

misalignments with customer needs.

The work of Amna and Poels (2022) describes that

ambiguity in natural language-based requirements,

especially in user stories, is a problem recognized by

the requirements engineering community due to lin-

guistic and cognitive issues. While some studies see

this ambiguity as intrinsic, there is a lack of standard-

ization and a limited understanding of the cognitive

factors that trigger it. An SLR by Amna (2022) high-

lighted three research gaps: the need for more stud-

ies on cognitive factors causing ambiguity, the low

number of studies on ambiguities in related user sto-

ries, and the lack of approaches addressing ambiguity

across linguistic levels.

Dar (2020) proposed using a gamiﬁcation tool to

address ambiguous and incomplete requirements, en-

hancing clarity, user engagement, and maintaining

their interest.

Putri et al. (2023) highlighted that the semi-natural

simplicity of user stories can lead to ambiguity, in-

consistencies, and incomplete statements, potentially

causing conﬂicts during development.

Xu et al. (2023a) noted that agile development

with user stories often faces incomplete, inconsistent,

and imprecise requirements, increasing workload and

reducing efﬁciency in addressing user requirements.

Alhaizaey and Al-Mashari (2023) highlighted that

poor deﬁnition or neglect of nonfunctional require-

ments is a common issue in agile projects. Limited

research, overly complex proposals, and unclear inte-

gration into agile environments hinder their adoption.

Wang et al. (2022) explored user story qual-

ity assessment, noting that while studies often ad-

dress grammatical defects, semantic defect veriﬁca-

tion remains underexplored. Dalpiaz and Brinkkem-

per (2021) identiﬁed linguistic defects as a quality is-

sue, which, while easily avoidable, are present in 50%

of real-world user stories, according to their study.

Thanomwong and Senivongse (2022) highlighted

that neglecting risks from low-quality user stories

early in development is a key factor in project failure,

affecting both development and management.

3.1.2 Derivation and Generation of Conceptual

Models

The section on conceptual model derivation explores

transforming user stories into structured representa-

tions for better requirement understanding.

Bragilovski et al. (2022) identiﬁed a research gap

in deriving user stories for conceptual design, while

Javed and Lin (2021) focus on extracting ER models

and business processes from informal requirements.

Gupta et al. (2023) proposed automating concep-

tual model generation from Behavior-Driven Devel-

opment scenarios, addressing challenges in convert-

ing behavioral descriptions into formal models. Also,

Gilson et al. (2020) addressed the problem of manu-

ally generating use case scenarios, which can be error-

prone and time-consuming in agile development.

Dalpiaz et al. (2021) explored transforming nat-

ural language requirements into formal models like

class diagrams, while Wu et al. (2022) automates the

creation of iStar models from user stories, enabling

analysis of objectives and actor dependencies.

unes¸ and Aydemir (2020) proposed using NLP

for automated goal model extraction from user sto-

ries, while Kolhatkar et al. (2023) focus on convert-

ing epics and user stories into pseudocode using trans-

formers.

3.1.3 Requirements Management and Task

Assignment

Requirements Engineering is experience-driven,

manual, and involves various speciﬁcation doc-

uments in different formats, such as business,

functional, interface, and customer speciﬁcations,

capturing critical product knowledge like features,

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

154

hierarchy, dependencies, and business context. Re-

quirements speciﬁcation is crucial for communication

within the development team, as each role has distinct

informational needs. The requirements engineer must

provide relevant information to prevent errors caused

by inadequate communication.

Specifying requirements is challenging due to the

large number of user stories in modern software

projects, complicating task management (Nistala

et al., 2022; Oran et al., 2021; Yang et al., 2023).

Vera-Rivera et al. (2020) highlight that optimized task

assignment is crucial for project success, reducing de-

velopment time and costs.

Nistala et al. (2022) proposed digitalizing require-

ments by generating context-sensitive user stories

from diverse speciﬁcations addressing the challenge

of standardizing and integrating various sources,

which can lead to inconsistencies and errors.

Yang et al. (2023) addressed the challenge of clas-

sifying and grouping user stories, where manual or-

ganization becomes difﬁcult as the number of stories

grows. Without automated tools, this can lead to in-

efﬁciencies, prioritization issues, and increased com-

plexity. They propose using machine learning (ML)

techniques for user story clustering.

Tsilionis et al. (2021a) explored the effectiveness

of conceptual modeling versus user story mapping,

highlighting the lack of clarity on which approach

best addresses communication, clarity, and organiza-

tion challenges in agile projects.

Ferrara et al. (2024) highlighted the lack of sys-

tematic support for fairness requirements engineer-

ing, highlighting the challenge of managing fairness

in AI systems due to the absence of speciﬁc tools in

traditional requirements engineering.

Casillo et al. (2022) emphasized the importance

of considering privacy attributes early in software de-

velopment, noting that developers often lack the ex-

pertise to integrate legal and social data protection

requirements. Villamizar et al. (2020) addressed the

challenge of reviewing security in agile requirements

for web applications, noting that security issues are

often overlooked due to the focus on quick delivery.

Urbieta et al. (2020) argued that agile methods

struggle with maintaining agility as requirements ac-

cumulate across sprints. Their work proposes to im-

prove requirements traceability by consolidating dis-

persed information into a structured lexicon.

Siahaan et al. (2023) highlighted that extracting

user stories from natural language improves require-

ments elicitation, but the approach remains limited.

3.1.4 Estimation

Agile models handle requirement changes ﬂexibly,

but frequent client requests can increase cost and

time. Various cost estimation techniques aim to ad-

dress inaccuracies, but precise estimates remain chal-

lenging (Butt et al., 2022). Factors like coordination,

team size, complexity, and daily meeting issues sig-

niﬁcantly raise costs and time, especially in Scrum

projects.

Effort estimation based on user stories is crucial

for success, yet user story inconsistencies, technical

complexities, and task practices contribute to inaccu-

racies. Butt et al. (2023) emphasized the need for

standardized protocols to improve estimation accu-

racy.

Butt et al. (2022) proposed a cost estimation tech-

nique based on developer expertise in Scrum, while

(Butt et al., 2023) focus on ML-based cost prediction.

Iqbal et al. (2024) highlighted the use of subjective

techniques like planning poker and size metrics for

effort estimation.

3.2 RQ 02: What Is the Method for

Solving this Problem?

Several articles apply NLP methods to address is-

sues, as suggested by Raharjana et al. (2021), with

objectives spanning defect detection to linking mod-

els and user stories. NLP studies in user stories aim to

discover defects, generate software artifacts, identify

core abstractions, and trace connections between the

model and user stories.

3.2.1 User Story Quality

Hallmann (2020) linked user story quality to shared

mental models and quality assessment criteria among

team members. This approach aims to uncover corre-

lations between quality variables, providing insights

to enhance agile requirements development and un-

derstanding cognitive processes in comprehending

user stories. This research can inform design recom-

mendations and AI tools to improve user story quality,

fostering better shared mental models and enhancing

collaboration and success in agile projects.

To address learning gaps, Amna and Poels (2022)

conducted a case study with 41 participants to com-

pare four models for creating and understanding user

stories. The results revealed signiﬁcant differences in

effectiveness, speed, and visual effort, but no model

emerged as superior.

Dar (2020) addressed requirement ambiguity by

developing a gamiﬁcation tool to elicit clear require-

ments while ensuring user engagement and interest.

Analyzing User Story Quality: A Systematic Review of Common Issues and Solutions

155

Putri et al. (2023) proposed using K-means and

feature selection to improve clustering performance

for user stories in agile development, with the Vari-

ance Threshold method enhancing results.

Xu et al. (2023a) proposed a quality assessment

method for user story-based requirements, including a

framework, model, and evaluation criteria. In another

study (Xu et al., 2023b), they introduced a method to

improve functional requirements quality in agile de-

velopment through a functional requirements model

and evaluation process.

Alhaizaey and Al-Mashari (2023) described a

framework using NLP and AI techniques to automate

the analysis and prediction of nonfunctional require-

ments in agile user stories.

Wang et al. (2022) proposed a multidimensional

framework to evaluate user story quality, focusing

on completeness, testability, and consistency. It

combines NLP techniques with iStar modeling-based

analysis to identify defects in these areas.

Gupta et al. (2022) focused on using conceptual

models to address challenges in agile requirements

engineering. These models visually represent sys-

tem functionalities and rules, enhancing understand-

ing and communication among stakeholders. The

authors advocate integrating conceptual models with

user stories to improve comprehension.

Thanomwong and Senivongse (2022) proposed a

model for prioritizing risks in user stories to enhance

risk awareness and mitigation. The model can be

combined with methods such as the Analytical Hier-

archy Process or weighted scoring to rank and priori-

tize risks associated with user stories.

Dalpiaz and Brinkkemper (2021) proposed tool-

supported methods to improve user story creation,

detailing the Quality User Story Framework and the

AQUSA tool in a tutorial format.

3.2.2 Conceptual Model Derivation and

Generation

Bragilovski et al. (2022) proposed deriving a holis-

tic view of structural and interaction aspects from

user stories, represented through class and use case

diagrams. They conducted a controlled experiment

with 77 undergraduate students using examples de-

rived from user stories.

Javed and Lin (2021) proposed iMER (Iterative

process of Entity Relationship and Business Process

Model Extraction), an iterative process that automat-

ically extracts ER and business process models from

requirements using NLP and semantic analysis. The

approach reﬁnes models as more information is pro-

cessed, improving efﬁciency and accuracy while eas-

ing the workload of analysts and developers.

Gupta et al. (2023) proposed an approach to au-

tomatically generate conceptual models (e.g., class,

use case, and activity diagrams) from BDD scenar-

ios using NLP. The method extracts key information

and converts it into visual representations, improving

consistency, efﬁciency, and quality.

Gilson et al. (2020) described an automated ap-

proach to generate use case scenarios from user sto-

ries using transformation rules. Validated through ex-

periments, the method enhances productivity and con-

sistency by converting informal user stories into for-

mal use-case scenarios.

Dalpiaz et al. (2021) evaluated techniques for

deriving conceptual models from user requirements

through experiments with professionals and students.

The study compared automated and manual methods,

assessing accuracy, completeness, and quality. Re-

sults showed that while automated approaches help,

challenges remain in interpreting natural language re-

quirements, with model quality varying by technique.

Wu et al. (2022) proposed an iStar model genera-

tion approach using NLP to streamline the extraction

of relationships between user stories. The method in-

volves extracting nodes from user stories and apply-

ing a BERT model to measure similarities between

these nodes, facilitating the identiﬁcation of relation-

ships. This approach aims to reduce the time required

for development teams to manually extract and map

concepts from diverse user stories, ultimately enhanc-

ing efﬁciency and improving the structuring of re-

quirements in agile projects.

unes¸ and Aydemir (2020) described an auto-

mated method using NLP to extract goal models from

user stories. The approach analyzes text to identify

key elements like goals and objectives, converting

them into formal models. Validated through experi-

ments, it demonstrates time savings and improved ac-

curacy in goal model creation, aiding system analysis

and development in agile projects.

Kolhatkar et al. (2023) proposed a methodology

to convert English problem descriptions into pseu-

docode using NLP. The process includes text-to-code

and code-to-pseudocode conversion. The study found

that the CodeT5 model achieved the highest BLEU

score when trained separately on these tasks, with

BLEU measuring the similarity between machine-

translated and reference translations.

3.2.3 Requirements Management and Task

Assignment

Nistala et al. (2022) proposed an automated approach

using NLP and semantic analysis to digitize require-

ments and generate context-sensitive user stories, im-

proving efﬁciency, accuracy, and consistency. Yang

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

156

et al. (2023) introduced a ML-based framework for

clustering user stories, automating feature classiﬁca-

tion into similarity-based groups, and addressing the

limitations of traditional clustering techniques.

Tsilionis et al. (2021a) compared conceptual mod-

eling and user story mapping using a Rationale Tree to

assess comprehensibility, requirement detection, and

adaptability. Also, Tsilionis et al. (2021b) analyzed

how rationale trees and user story maps improve soft-

ware issue organization, clarity, and traceability, ﬁnd-

ing both techniques valuable, with strengths depend-

ing on project context and needs.

Oran et al. (2021) introduced the ReComP frame-

work to address issues in requirement communication

artifacts, specifying informational needs for each de-

velopment role and suggesting improvements. Vali-

dated through independent studies, ReComP reduced

user story issues by 77% and eliminated all issues in

use cases identiﬁed by testers.

Ozcelikkan et al. (2022) developed a multi-

objective Scrum planning model that optimizes sprint

capacity, prioritizes user stories, and clusters related

stories using NSGA-II and SPEA2 heuristics. The

model improves planning, estimation, and manage-

ment by optimizing business value, time, cost, and

quality. Ferrara et al. (2024) introduced ReFAIR, a

contextual recommender that uses NLP to identify

and incorporate fairness requirements into software

projects, proving its effectiveness in experiments.

Casillo et al. (2022) proposed an approach com-

bining NLP, linguistic features, and deep learning

(DL) to identify privacy aspects in user stories. NLP

extracted semantic and syntactic information, which

was processed by a pre-trained convolutional neural

network using transfer learning. The approach was

evaluated with 1,680 user stories.

Vera-Rivera et al. (2020) aimed to automate and

optimize user story assignments based on team mem-

bers’ experience (junior, senior, or novice). The pro-

posed Java algorithm was validated in a real-world

case study.

Urbieta et al. (2020) proposed a traceability ap-

proach using an index structure to access user stories.

The method extracts and organizes scattered informa-

tion into symbols within the Language Extended Lex-

icon (LEL).

Villamizar et al. (2020) proposed an approach to

review security aspects in agile web application re-

quirements by linking user stories to security proper-

ties using NLP.

3.2.4 Estimation

Butt et al. (2023) proposed a cost estimation tech-

nique based on user story complexity and developer

experience, validated through two projects of vary-

ing sizes. Butt et al. (2022) described a framework to

control cost overruns and schedule deviations, tested

in several software industry case studies. Their re-

sults showed that the estimation technique improved

project accuracy by mitigating development issues.

3.2.5 Summary

Based on the ﬁndings presented above, Table 2 sum-

marizes the issues and solutions identiﬁed in the SLR.

The results from the SLR indicate that the most

frequent issue in user stories is related to quality, with

15 works. This is followed by challenges in require-

ments management and task assignment (12 works)

and derivation and generation of conceptual model

(8). Estimation was the least frequently mentioned

issue, appearing only three times, suggesting a poten-

tial gap that warrants further research.

Regarding solution methods, NLP, ML and AI

were the most commonly employed, cited in 15

works, highlighting the signiﬁcant role of AI in ad-

dressing these challenges.

It is important to note that some studies only de-

scribe problems without presenting solution methods.

On the other hand, other studies only present solutions

without describing the problems.

Table 2: Issues and solutions identiﬁed in the SLR.

Issues Works Solutions

User Story

Quality

Systematic method (4)

Case study (1)

Gamiﬁcation (1)

NLP, ML, or AI (3)

Conceptual models (2)

Derivation/

Generation of

Case Study (2)

NLP, ML or AI (6)

Requirements

Management

and Task

Assignment

NLP, ML or AI (6)

Empirical experimenta-

tion (2)

Framework (1)

Multi-objective model

(1)

Algorithm (1)

Traceability approach (1)

Estimation 3 Framework (2)

4 CONCLUSION

In this work, we conducted a Systematic Literature

Review (SLR) from 2020 to 2024 to identify key

problems and solutions in user stories. While the time

Analyzing User Story Quality: A Systematic Review of Common Issues and Solutions

157

range may limit the analysis, it allowed us to focus on

recent, relevant issues.

We found a balance between three main problem

groups: User Story Quality, Derivation and Genera-

tion of Conceptual Models, and Requirements Man-

agement and Task Assignment, with a slight emphasis

on quality-related issues. This highlights the growing

importance of user story quality in practice.

Natural Language Processing (NLP) and AI tech-

niques were frequently used to address these chal-

lenges. However, the review also revealed a range

of alternative strategies, including systematic meth-

ods and frameworks, particularly for estimation prob-

lems. These methods complement AI solutions by

emphasizing structured practices and frameworks for

more precise requirement deﬁnition.

The main limitation of this study is the temporal

scope, which may exclude earlier or more recent stud-

ies, affecting the comprehensiveness of the ﬁndings.

Future work will use this knowledge to develop solu-

tions for issues in user story speciﬁcation.

ACKNOWLEDGEMENTS

We thank the Brazilian Army and its Army Strategic

Program ASTROS for the ﬁnancial support through

the SIS-ASTROS GMF project (898347/2020).

REFERENCES

Alhaizaey, A. and Al-Mashari, M. (2023). A framework for

reviewing and improving non-functional requirements

in agile-based requirements. In 2023 18th Iberian

Conference on Information Systems and Technologies

(CISTI), pages 1–7. IEEE.

Amna, A. (2022). Identifying ambiguity problems in user

stories: A proposed framework. In CEUR Work-

shop Proceedings, Belgium. CEUR Workshop Pro-

ceedings.

Amna, A. R. and Poels, G. (2022). Ambiguity in user sto-

ries: A systematic literature review. Information and

Software Technology, 145:106824.

Bakare, A., Ciancarini, P., Farina, M., Kruglov, A.,

Okonicha, O., Smirnova, M., and Succi, G. (2023).

Learning from west african storytellers. Frontiers in

Computer Science, 5:1183602.

Bragilovski, M., Dalpiaz, F., and Sturm, A. (2022). Guided

derivation of conceptual models from user stories: A

controlled experiment. In International Working Con-

ference on Requirements Engineering: Foundation for

Software Quality, pages 131–147, Cham. Springer In-

ternational Publishing.

Butt, S. A., Ercan, T., Binsawad, M., Ariza-Colpas, P. P.,

Diaz-Martinez, J., Pineres-Espitia, G., et al. (2023).

Prediction based cost estimation technique in agile

development. Advances in Engineering Software,

175:103329.

Butt, S. A., Khalid, A., Ercan, T., Ariza-Colpas, P. P.,

Melisa, A. C., Pi

neres-Espitia, G., and Ortega, R. M.

(2022). A software-based cost estimation technique

in scrum using a developer’s expertise. Advances in

Engineering Software, 171:103159.

Casillo, F., Deufemia, V., and Gravino, C. (2022). Detect-

ing privacy requirements from user stories with nlp

transfer learning models. Information and Software

Technology, 146:106853.

Da Silva, A. R. and Savi

c, D. (2021). Linguistic patterns

and linguistic styles for requirements speciﬁcation:

Focus on data entities. Applied Sciences, 11(9):4119.

Dalpiaz, F. and Brinkkemper, S. (2021). Agile requirements

engineering: from user stories to software architec-

tures. In 2021 IEEE 29th International Requirements

Engineering Conference (RE), pages 504–505. IEEE.

Dalpiaz, F., Gieske, P., and Sturm, A. (2021). On deriving

conceptual models from user requirements: An em-

pirical study. Information and Software Technology,

131:106484.

Dar, H. S. (2020). Reducing ambiguity in requirements

elicitation via gamiﬁcation. In 2020 IEEE 28th Inter-

national Requirements Engineering Conference (RE),

pages 440–444. IEEE.

Ferrara, C., Casillo, F., Gravino, C., De Lucia, A., and

Palomba, F. (2024). Refair: Toward a context-aware

recommender for fairness requirements engineering.

In Proceedings of the IEEE/ACM 46th International

Conference on Software Engineering, pages 1–12.

IEEE/ACM.

Gilson, F., Galster, M., and Georis, F. (2020). Generating

use case scenarios from user stories. In Proceedings of

the International Conference on Software and System

Processes, pages 31–40. ACM.

Gralha, C., Pereira, R., Goul

ao, M., and Araujo, J. (2022).

Assessing user stories: The inﬂuence of template dif-

ferences and gender-related problem-solving styles.

Requirements Engineering, 27(4):521–544.

unes¸, T. and Aydemir, F. B. (2020). Automated goal model

extraction from user stories using nlp. In 2020 IEEE

28th International Requirements Engineering Confer-

ence (RE), pages 382–387. IEEE.

Gupta, A., Poels, G., and Bera, P. (2022). Using conceptual

models in agile software development: A possible so-

lution to requirements engineering challenges in agile

projects. IEEE Access, 10:119745–119766.

Gupta, A., Poels, G., and Bera, P. (2023). Generating mul-

tiple conceptual models from behavior-driven devel-

opment scenarios. Data & Knowledge Engineering,

145:102141.

Hallmann, D. (2020). “i don’t understand!”: Toward a

model to evaluate the role of user story quality. In

Agile Processes in Software Engineering and Extreme

Programming: 21st International Conference on Ag-

ile Software Development, XP 2020, Copenhagen,

Denmark, June 8–12, 2020, Proceedings, pages 103–

112. Springer International Publishing.

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

158

Iqbal, M., Ijaz, M., Mazhar, T., Shahzad, T., Abbas,

Q., Ghadi, Y., et al. (2024). Exploring issues of

story-based effort estimation in agile software devel-

opment (asd). Science of Computer Programming,

236:103114.

Javed, M. and Lin, Y. (2021). imer: Iterative process of

entity relationship and business process model extrac-

tion from the requirements. Information and Software

Technology, 135:106558.

Kitchenham, B. and Charters, S. (2007). Guidelines for per-

forming systematic literature reviews in software en-

gineering. Technical Report EBSE 2007-001, Keele

University and Durham University Joint Report.

Kolhatkar, G., Madan, A., Kowtal, N., Roy, S., and Son-

awane, S. (2023). Converting epics/stories into pseu-

docode using transformers. In 2023 IEEE 20th India

Council International Conference (INDICON), pages

37–42. IEEE.

Kustiawan, Y. A. and Lim, T. Y. (2023). User stories in re-

quirements elicitation: A systematic literature review.

In 2023 IEEE 8th International Conference On Soft-

ware Engineering and Computer Systems (ICSECS),

pages 211–216. IEEE.

Nistala, P. V., Rajbhoj, A., Kulkarni, V., Soni, S., Nori,

K. V., and Reddy, R. (2022). Towards digitalization

of requirements: generating context-sensitive user sto-

ries from diverse speciﬁcations. Automated Software

Engineering, 29(1):26.

Oran, A. C., Santos, G., Gadelha, B., and Conte, T.

(2021). A framework for evaluating and improving

requirements speciﬁcations based on the developers

and testers perspective. Requirements Engineering,

26(4):481–508.

Ozcelikkan, N., Tuzkaya, G., Alabas-Uslu, C., and Sen-

naroglu, B. (2022). A multi-objective agile project

planning model and a comparative meta-heuristic

approach. Information and Software Technology,

151:107023.

Putri, R. A., Yuhana, U. L., and Amri, T. C. (2023). K-

means and feature selection mechanism to improve

performance of clustering user stories in agile devel-

opment. In 2023 International Conference on Mod-

eling & E-Information Research, Artiﬁcial Learning

and Digital Applications (ICMERALDA), pages 39–

43. IEEE.

Raharjana, I. K., Siahaan, D., and Fatichah, C. (2021). User

stories and natural language processing: A systematic

literature review. IEEE Access, 9:53811–53826.

Siahaan, D., Raharjana, I. K., and Fatichah, C. (2023).

User story extraction from natural language for re-

quirements elicitation: Identify software-related infor-

mation from online news. Information and Software

Technology, 158:107195.

Thanomwong, P. and Senivongse, T. (2022). User story risk

prioritization model for agile software development.

In 2022 International Conference on Data and Soft-

ware Engineering (ICoDSE), pages 161–166. IEEE.

Tsilionis, K., Maene, J., Heng, S., Wautelet, Y., and Poel-

mans, S. (2021a). Conceptual modeling versus user

story mapping: Which is the best approach to agile

requirements engineering? In International Confer-

ence on Research Challenges in Information Science,

pages 356–373. Springer International Publishing.

Tsilionis, K., Maene, J., Heng, S., Wautelet, Y., and Poel-

mans, S. (2021b). Evaluating the software problem

representation on the basis of rationale trees and user

story maps: premises of an experiment. In Soft-

ware Business: 11th International Conference, IC-

SOB 2020, Karlskrona, Sweden, November 16–18,

2020, Proceedings 11, pages 219–227. Springer In-

ternational Publishing.

Urbieta, M., Antonelli, L., Rossi, G., and do Prado Leite, J.

C. S. (2020). The impact of using a domain language

for an agile requirements management. Information

and Software Technology, 127:106375.

Vera-Rivera, F. H., Barbosa Mora, J. L., and Gaona, C.

(2020). Generaci

on autom

atica de la planiﬁcaci

on de

la entrega en desarrollo de software agil, asignaci

de historias de usuario a los desarrolladores usando

algoritmos gen

eticos. AiBi Revista de Investigaci

on,

Administraci

on e Ingenier

ıa, 8(2):29–38.

Villamizar, H., Kalinowski, M., Garcia, A., and Mendez,

D. (2020). An efﬁcient approach for reviewing

security-related aspects in agile requirements speciﬁ-

cations of web applications. Requirements Engineer-

ing, 25(4):439–468.

Wang, T., Wang, C., Li, T., Liu, Z., and Zhai, Y.

(2022). User story quality assessment based on multi-

dimensional perspective: A preliminary framework.

In iStar, pages 7–13.

Wu, C., Wang, C., Li, T., and Zhai, Y. (2022). A node-

merging based approach for generating istar mod-

els from user stories. In Software Engineering and

Knowledge Engineering, pages 257–262.

Xu, X., Dou, Y., Qian, L., Jiang, J., Yang, K., and

Tan, Y. (2023a). Quality improvement method for

high-end equipment’s functional requirements based

on user stories. Advanced Engineering Informatics,

56:102017.

Xu, X., Dou, Y., Qian, L., Zhang, Z., Ma, Y., and Tan, Y.

(2023b). A requirement quality assessment method

based on user stories. Electronics, 12(10):2155.

Yang, B., Guo, H., and Liu, H. (2023). Evaluation and as-

sessment of machine learning based user story group-

ing: A framework and empirical studies. Science of

Computer Programming, 227:102943.

Analyzing User Story Quality: A Systematic Review of Common Issues and Solutions

159