=Paper=
{{Paper
|id=Vol-3045/paper05
|storemode=property
|title=An Empirical Study on Data-driven Requirements Elicitation: Reflections from Nordic Enterprises
|pdfUrl=https://ceur-ws.org/Vol-3045/paper05.pdf
|volume=Vol-3045
|authors=Alejandro Martinez,Marcus Melin,Georgios Koutsopoulos,Jelena Zdravkovic
|dblpUrl=https://dblp.org/rec/conf/ifip8-1/MartinezMKZ21
}}
==An Empirical Study on Data-driven Requirements Elicitation: Reflections from Nordic Enterprises==
https://ceur-ws.org/Vol-3045/paper05.pdf
An Empirical Study on Data-driven Requirements Elicitation:
Reflections from Nordic Enterprises
Alejandro Martinez1, Marcus Melin2, Georgios Koutsopoulos2 and Jelena Zdravkovic2
1
Nexer Group AB, Valhallavägen 117G, 11553, Stockholm, Sweden
2
Department of Computer and Systems Sciences, Stockholm University, Borgarfjordsgatan 12, 16407, Kista,
Stockholm, Sweden
Abstract
There is a plethora of digital data sources that may be exploited for collecting requirements for
system development and evolution. In contrast to human sources, i.e. stakeholders, digital
sources continuously generate data that is often not originally created for the purposes of
requirements elicitation, e.g. on forums, microblogs, machine-generated trace logs, and sensor
data. Streams of large volumes of data can be exploited to enable automation of a continuous
requirements elicitation process using AI techniques that combine natural language or machine
data processing, with machine learning. On the other hand, the complex characteristics of big
data due to its size, lack of structure, high dynamics, and low predictability, present numerous
challenges on the process of extracting requirements-related information that would be of a
clear value for companies. The purpose of this interview study was to, from the practitioners’
perspective, elicit their overall expectations and needs for a method for the elicitation of system
requirements from digital data sources. Semi-structured interviews were conducted with
several industrial experts from different business domains and the collected empirical data has
been analyzed using thematic analysis. The results lead to the identification of a set of high-
level requirements related to the method for the elicitation from digital data sources.
Keywords 1
Data-driven Requirements Engineering, Big Data, Requirements Elicitation, Agile
Requirements Engineering, Enterprise Modeling
1. Introduction
Requirements engineering is a fundamental activity in the system development process of systems, as
well as it is seen as a critical part of a project's success as many software deficiencies have their origin
and can be traced back to the requirements [1]. Some of these deficiencies are due to requirements not
contributing to creating the system that users need or request, or because of incomplete requirements,
created or interpreted in a subjective way by the development team. The goal of requirements elicitation
is to collect requirements of all relevant stakeholders. Furthermore, the requirements are documented,
developed, and then frequently changed in the evolution part of the system’s development life-cycle.
Owing to the digital transformation of the business sector, industries, and society in general — and
the subsequent emergence of big data — the interest to consider digital sources of information for
requirements has emerged, in addition to the traditional stakeholder-driven elicitation. As a result, there
are ongoing efforts to support and enrich requirements elicitation activities by automatically processing
and mining digital data for information about requirements [2, 3]. The main motivation behind Data-
driven Requirements Engineering (DdRE) is to take advantage of large amounts of digital data related
to a system in order to guide and support requirements engineers in their decisions about which
requirements to include in subsequent system releases [4].
PoEM’21 Forum: 14th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modelling, November 24–26, 2021, Latvia
EMAIL: alejandro.martinez@sigma.se (A. Martinez); marcus.melin1991@gmail.com (M. Melin); georgios@dsv.su.se (G. Koutsopoulos);
jelenaz@dsv.su.se (J. Zdravkovic)
ORCID: 0000-0003-2511-9086 (G. Koutsopoulos); 0000-0002-0870-0330 (J. Zdravkovic)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
39
Yet, the characteristics of big data in terms of volume, velocity, variety, and veracity present many
challenges for effectively collecting and understanding requirements-related information, requiring
different data processing techniques, as well as the identification of appropriate analytical algorithms.
These decisions are highly dependent on the types of digital source that are targeted. Furthermore, as
the data is not created explicitly for requirements elicitation, it tends to be limited in terms of
completeness and correctness in respect to what is understandable as a system requirement and feasible
to develop and implement. Therefore, the data-driven elicitation may require a substantial manual effort
[5], or could be of low performance and therefore low practical use [6].
While many companies are increasingly interested in exploiting the potential value and opportunities
that these data sources provide for requirements engineering, they face the challenge of understanding
how to manage big data efficiently, as well as to how to integrate it with the current development
methods in place. Previous research points that neither traditional nor agile development methods
explicitly define how the collection and documentation of requirements should be done when the
requirements are determined on the basis of diverse big data sources [2]. Furthermore, [7, 8, 9] advocate
that there is a great need to define a complementary process for guiding how to use big data for
requirements elicitation, where agility is considered relevant in this case as big data is created
continuously over time, and under different business conditions. Therefore, the goal of this paper is to
present an interview study devoted to industrial requirements for a data-driven RE method. The study
is a part of an ongoing design science research project on this topic [7, 9, 10].
The rest of the paper is organized as follows. Section 2 presents a background on the dominant RE
methods and the recent research in data-driven RE. Section 3 presents the method applied for
conducting this semi-structured interview study. In section 4, results of the thematic analysis are
reported together with the elicited requirements for data-driven requirements elicitation. Section 5
provides a discussion on the findings, while section 6 concludes the paper and outlines future research.
2. Background
Requirements engineering methods exist for decades. The best known is the plan-driven method, a
sequential approach to system development that was introduced in the 70s, also known as “waterfall”.
Two decades later, an incremental development approach emerged. In this approach the requirements
are elicited in iterations where the core system functionality is defined in the first increments, and other
features and quality requirements are left for later iterations. Based on this approach, the agile
development method was proposed during the 2000s [11,12]. The method advocated continuous
communication with the user, late and limited documentation, frequent system releases, and quick
reactions to changes. In all methods, the elicitation phase implies obtaining intended statements related
to a system under development that are transformed to a canonical format, which is understandable to
software teams and feasible to develop and implement, such as to the semi-formal requirement structure
in the plan-based approach [1] or to the user story template in agile approaches [12].
Data-driven RE has emerged during the last few years due to a wide range of online digital sources
that may be exploited for requirements elicitation [2, 4]. Increasing attention has been given to sources
that are more dynamic, i.e. continuously generating large amounts of data through various mediums
and platforms, such as online discussion forums, app reviews, and microblogs, as well as to sensor data
and machine-generated logs. Most of the efforts have been focused on identifying and classifying
requirements-related information from few sources such as user feedback and machine-generated data.
A systematic literature review showed that there is still lack of integration with the existing system
development methods [3]. Recently, more holistic views on data-driven RE have been presented, which
integrate information from different sources, based on domain ontologies [2], metamodeling [10], or
contextual factors [9]. Although these studies address relevant research challenges, they pay less
attention to the practitioners’ views and challenges with data-driven RE, which is still scarce and ad-
hoc applied. Therefore, the aim of this study has been to investigate how practitioners see the challenge
of eliciting data-driven requirements, and should it be methodologically supported.
When the digital data is in the form of free text, Natural Language Processing (NLP) is applied to
extract requirements information. Identification of information in free text is commonly performed by
the Named Entity Recognition (NER) task that concerns classifying word sequences to identify
40
requirements-related information, such as feature mentions. Machine Learning (ML) is applied to learn
from historical data to perform the classification of requirements-related data. This learning process is
typically supervised and relies on manual annotation of examples. This requires effort, but once an ML
model has been created, it can be applied repeatedly to facilitate automation of requirements elicitation.
3. Method
This study is part of a design science research (DSR) project which aims to develop a method for data-
driven requirements elicitation and management. According to the DSR methodology [13], exploratory
semi-structured interview studies may be used for iterative requirements analysis of the design artefact.
The exploratory semi-structured interviews of this study followed an interview guideline with a set of
predetermined open-ended questions. The questions were structured into three themes – current
elicitation methods in use, the relevance of big data, and the viewpoints on the data’s potential for
improving requirements elicitation. Using thus a deductive approach, a set of interview questions were
derived for each of these three themes. A thematic analysis was used to analyze the answers on the
given questions - to annotate and collate the interview transcriptions and summarize the findings using
the qualitative data analysis. The categories and codes were created based on the interview data, i.e.
citations, further giving rise to a set of high-level requirements related to the approach to the elicitation
of data-driven requirements. Purposive sampling was conducted in order to select study participants
among industry experts, where the respondents with different professional roles have been chosen in
order to ensure a comprehensive analysis basis; selection criteria were: a) the respondent has a long
work experience in IT; the respondent has >3 years' work experience in big data; the respondent has a
long experience and expertise with requirements management. Each interviewee received one ~2 hour-
long interview session (Table 1) starting with a brief introduction of data-driven RE research.
Table 1
Description and identification codes of the interviewees
ID Description of interviewee (respondent)
I1 Business Intelligence Connoisseur in a consulting company in the data-driven business
transformation.
I2 Business Intelligence Developer in a Nordic IT company that offers solutions for business
operations and management of IT services.
I3 Senior Test Consultant in a consulting company for digital services and enterprise
information systems.
I4 Agile Coach in the world’s largest music streaming and media services provider.
I5 Chief Architect in a company providing integrated solutions for dairy and farming machinery
production.
I6 CTO of the company developing and publishing themed strategy video games.
I7 Requirements Analyst in an online pan-Nordic financial services company.
4. Results
In this section, the categories and codes that emerged during the thematic analysis of the interviews’
data and the associated themes are presented. From the three themes - Method, Big Data, and Viewpoint
and the corresponding questions, seven categories and 15 essential codes transpired. Based on the
analysis, 16 requirements concerning a data-driven RE method were elicited.
All the respondents confirmed the need for using big data when collecting requirements.
Furthermore, attention was paid to the consideration of big data sources - most of the companies already
have access to a lot of data from several different types of digital sources but barely utilize them for
requirements elicitation. At the same time, the respondents confirmed that the digitalization that takes
place in the IT industry gives rise to new forms of requirements collection.
41
4.1. Theme: Method
The theme Method captures the respondents' knowledge on the use of big data for requirements, as well
as their experiences of working with methods for system development, and in particular – requirements
elicitation. All respondents confirmed that they do not have any systemized methods for collecting
requirements from big data. The theme includes two categories of analyzed data, Digitalization (Table
2) and Approach to requirements elicitation (Table 3).
Table 2
Category: Digitalization
Code Citation
Use of big data “When you use sensors, you kind of try to capture exactly what's going on, all of
for requirements them here and get as a digital source and be able to read from…I can imagine
elicitation. just in zoos that it will be as large volumes of data that you want to be able to see
Motivates R1 what is actually needed based on that and like all these interactions, as well as,
what the animals do, what happens to all machines - that there are many as digital
footprints that would contribute to new requirements.” (I2)
Efficiency in “Data should give us value and then you draw up a technical solution to get that
requirements data and then you still sit and think that you should have something that has power
elicitation from of BI or something else. Then I will analyze data and find these patterns and I
big data. think there will be much more computer-analyzed solutions.” (I3)
Motivate R1 and “I think all such smart tasks, requirements collection etc... come with
R2 digitalization so all Big-Data should be used. It creates the conditions for doing
all that stuff. That's where performance really becomes important to check” (I1)
“Digital user data is giving us the ability to monitor and act on the evolution of
the requirements of our player base, particularly in order to improve
performance, automation, and objectivity for new releases.” (I6)
The respondents observe that the use of big data has a potential for improved requirements
elicitation. They argue that the big data will provide value if it is used in a way that will improve
performance in the elicitation – by automation of the collection, as well as by elicitation from a larger
and wider user base. Based on the codes collated in this category, requirements R1 and R2 are elicited
as follow:
R1: There shall be a method to enable the collection and analysis of digital data from different digital
sources, in an automated manner.
R2: The method shall enable repetitive collection and analysis tasks.
Table 3
Category: Approach to requirements elicitation
Code Citation
Fit to existing “It's a mix of methods. Yes, we are quite too big changes, we are still quite project-
approaches to driven.” (I2)
development. “We try follow a product centered approach with inspiration from DevOps..” (I4)
Motivates R3 “We have product owner that works a lot according to the waterfall method. So
many waterfalls… sequential” (I3)
“Large solutions need a deep understanding of successful requirements
management” (I5)
“We try to work agile but I would probably say that it is 50% agile” (I3)
42
Highly iterative “We work towards the smallest feasible product development, frequent
requirements deliveries” (I1)
elicitation. “So yes, we want to move more towards becoming more agile with new
Motivates R4 requirements but we are not there today.” (I7)
“There should be continuous cycles of iterations” (I5)
“We need all time to know how our players think, how they play, to fix problems
in the release…and to constantly please them by adding cool things...” (I7)
The respondents say that they need to combine different methods to be able to achieve some arbitrary
results. At the same time, the respondents want to move towards agile methods, but this requires
commitment from the entire business (organization). Constantly changing big data is the main reason
why several respondents want to move towards more agile alternatives, with the possibility to work in
short sprints for requirements collection and continuously analyze the needs. Based on the codes
collated in this category, requirements R3 and R4 are elicited as follow:
R3: The method shall be compatible with the organization’s approach to system development.
R4: The method shall provide adequate support for short and continuous iterations for system
development and evolution.
4.2. Theme: Big Data
The theme Big Data captures the respondents' different types of big data sources that are available, the
maintenance of data quality and the forms of big data analysis for identifying new needs and developing
requirements. During the interviews, the respondents stated that they have access to different types of
big data sources. The theme includes three categories of analyzed data, Data sources (Table 4), Data
quality (Table 5), and Data analysis (Table 6).
Table 4
Category: Data sources
Code Citation
Consideration of “It's one of the shortcomings that we may still have, but that I think people have
any available started to realize, is that we have too little customer involvement in our
big data source. development.” (I2)
Motivates R5 “We should use most big data sources…large scope that identifies mass gaps that
need to be addressed” (I1)
“Any customer transaction is a relevant data source” (I4)
"You should use sensors, to kind of try to capture exactly what is happening, and
get as a digital source" (I2)
“Processing of NL sources is prioritized in our company due to their dominant
quantity. However, we also utilize an eye-tracker sensor service for obtaining
various statistics on players’ behavior, i.e. concerning eye gazes and moves. Even
the documents concerning ethics and privacy need to be watched in relation to
the requirements constraints for game features” (I6)
“We have high interest to increase objectivity of management of players’ feedback
by collecting data from even other highly relevant sources, primarily from forums
and Twitter. “
The respondents commented on several alternative sources that could be used to understand the
users’ needs related to the software product. This new form of data collection would result in a better
and more accurate system development. The respondents believe that any digital source is worth
43
considering, or having in mind for the future. Based on the codes collated in this category, requirement
R5 is elicited as follow:
R5: The method shall enable the collection of data from both user- and machine-based digital data
sources.
Table 5
Category: Data quality
Code Citation
Identification of “The main idea is to understand what you really need to understand and
relevant data. prioritize, that is, what is actually your user base” (I5)
Motivates R6 “Yes, the data quality really varies…everyone understands that it is important,
but sometimes it costs much. Data management is a matter of priority.” (I1)
Use of methods “Data needs to be sorted and filtered” (I2)
for ensuring “We need to understand data, make it usable” (I6)
data quality. “The use of bad methods can lead to major data quality problems.” (I3)
Motivates R7 “It is difficult to draw conclusions on big data, it needs to be structured and
accurate” (I4)
Data quality is a factor that all the respondents consider important to systematically address. It is
considered that one must sort collected data for the best possible use, which is something that is lacking
for many respondents’ companies. If the business does not have well-defined processes and techniques
for handling data, the data quality will be lacking. Based on the codes created in this category,
requirements R6 and R7 are elicited as follow:
R6: The method shall prioritize data and the respective digital sources based on their relevance for the
organization.
R7: The method shall include method components specialized for improving the quality of data without
altering their meaning.
Table 6
Category: Data analysis
Code Citation
Documentation “You need to so see if the strategy is successful - if big data shows good results.”
and structure of “Yes, it's much about the processes that exist for all data sources, if you have
data analysis good processes and routines in place. That is, if things are well applied and
means. documented, then you usually have fewer problems with data quality. While if you
Motivates R8 have poor control of your data and do not really understand how you got what,
then you are in trouble” (I3)
Learn patterns “Big data analysis is to identify behavior” (I4)
in data analysis. “When requests for changes emerge different disclaimers need to be checked and
Motivates R9 added, so they need to be in the regulatory and applied.” (I7)
“To be effective we need to identify patterns in big data with AI” (I1)
Predict new “We should be able to anticipate user needs and desires from current user
features. behavior and comments” (I2)
Motivates R10 “We should be able to analyze user data to find strategies to increase software
usage” (I5)
The majority of the respondents highlighted how big data could contribute with substantial
advantages in system development and requirements collection. There are large amounts of data that
are constantly generated and as such should be kept as the basis for broad and deep analyses. This data
44
could, for example, be used to develop repeatable analysis techniques. One important aspect is to
identify and find patterns in big data in order to later be able to reap substantial benefits. Finally, the
respondents believe that big data can be used to provide users with a need that they themselves were
not aware of based on their behavior when using a software product. Based on the codes created in this
category, requirements R8, R9, and R10 are elicited as follow:
R8: The method shall be able to store and assess the methods and models for data analysis.
R9: The method shall be able to discover patterns during data analysis.
R10: The method shall include data analysis components for predicting users’ future needs.
4.3. Theme: Viewpoint
The theme Viewpoint captures the respondents' different opinions, experiences, and position on the
requirements collection from big data. Many respondents discuss the future of requirements
management and how it can possibly be affected by alternative sources than just traditional
stakeholders. The theme includes two categories of analyzed data, Opportunities (Table 7) and
Challenges (Table 8).
Table 7
Category: Opportunities
Code Citation
Continuous “It just increases and increases with the amount of data. It becomes as natural
requirements that it becomes more and more. Not just more and more sources” (I3)
collection. “But yes, it feels like a sequel, if you can call it the 'Big Data World'” (I2)
Motivates R11 “One needs to act on big data whole time…” (I1)
and R12 “Big data is relevant for continuation of requirements management, for software
improvement” (I2)
“We should be able to control a behavior subconsciously….” (I5)
Data “Big data represents population level well.” (I4)
availability. "Then you had a completely different opportunity thanks to all this data storage"
Motivates R13 (I1)
Identification of “We want to know what our users are missing in our products! (I6)
new behavior. “We need to know how exactly we could improve our online services” (I7)
Motivates R14 “We should be able to analyze user data to find strategies to increase software
usage” (I5)
“Users’ sentiments are critical, for example negative one to automatically detect
and have fast reaction when the number or impact of the players who express it
is significant; this is important!” (I6)
All the respondents have identified a need to use big data sources when collecting requirements.
With the advance of digitalization, the amounts of data increase and lead to new forms of data sources
that can be used. You can discover new information that you did not have access to before. This is
summed up as a continuation of requirements collection with big data, a future situation that should be
used. Based on the codes created in this category, requirements R8, R9, and R10 are elicited as follow:
R11: The method shall be able to continuously acquire relevant data from the respective sources.
R12: The method shall be able to combine data associated to similar behavior.
R13: The method should store collected data into a storage to enable analysis and learning.
R14: The method shall be able to recognize behavioral variations.
45
Table 8
Category: Challenges
Code Citation
Difficulty to “Data-driven is new…it is difficult to handle large amounts of unstructured data”
process big data. (I2)
Motivates R15 “It is simpler with traditional stakeholders…here there are no processes and the
type of different data you have to take in that way will be more difficult.” (I1)
“Fixed routines are needed for the requirements management for big data” (I3)
"So what you constantly dig in is some kind of historical rubbish heap and you
have to be aware of that, there are absolutely many things that are good to find
there." (I4)
Data is many, “…unstructured data cannot be used…” (I5)
unstructured, “…it is difficult to understand big data requirements…” (I1)
immature. “Yes, it is well with this as then if it is AI in that it is difficult to as well as for the
Motivates R16 important thing when you have a requirement is also to understand what is the
expected result then. What do we think this will give us?” (I3)
The respondents suggest that collecting requirements from big data will be a challenge. It is a
difficult process to handle large amounts of data and it is important for businesses to have clear methods
and processes for handling the data. This is because there is a lot of data that is generated and does not
always mean something, neither is relevant, nor adds any value. Two respondents believe that the degree
of maturity of the business defines how easy or difficult such work with requirements collection will
be. Finally, a respondent also emphasizes the importance of keeping in mind that one only analyzes
historical data, which can be seen as a "historical rubbish heap". One cannot always predict the need
for the future as it is old data that risks giving a misleading picture of a situation.
R15: The method should be supported be a systematic process, with defined steps and guidelines.
R16: The method should enable creating meaningful data by the use of AI techniques.
Figure 1 summarizes the requirements collected from the thematic analysis, and which codes,
categories, and themes, they concern:
,
Figure 1: A map including the themes, categories and codes, along with the elicited requirements.
46
5. Discussion of Results
The themes that have been identified in this exploratory study reflect the state of the field. The experts
expressed their requirements for development of the field in aspects related to methods, which relate to
“how” questions and the procedure of eliciting requirements from big data, in aspects related to the big
data itself, which reflect “what” questions that are associated to the nature of big data, and in specific
aspects of viewpoints that reflect “why” and “why not” questions, in terms of opportunities and
challenges involved in the process of transitioning towards a big data-driven state. Regarding the
derived requirements in relation to the themes, there seems to be a balance between big data and
viewpoints, since six requirements have originated from each theme and slightly less, in particular, four,
have been derived from the method theme. This is a possible indication on which aspects of DdRE
require more research attention and are of greater concern to the users, but this is a mere indication that
should be further researched in future steps of this project for verification.
The experts that participated in this study have provided important insight towards the development
of a specification for a method that aims to utilize big data to elicit requirements. However, apart from
the direct answers and information, there seems to be a latent theme expressed by the participants. A
finding that should not be neglected is the fact that, among the interviewees, a consensus exists
regarding the complete absence of established methods for requirements collection with big data in their
companies. This fact, in conjunction with their stated consensus on attempting to explore the field,
seems to indicate a contradiction. All the interviewees are experts in the field, which means that they
are aware of the benefits of adjusting to the new situation. They also state that they are willing to adopt
data-driven requirement elicitation with big data, but there seem to be factors hindering the adoption
that need to be identified and researched. One such potential hindrance may be the infamous “resistance
to change” expressed during these exploratory interviews in terms of challenges related to the nature
and processability of big data, and also in terms of compatibility with existing development approaches.
Combined with tradition, resistance to change can be a strong factor that needs to be taken into
consideration in all similar research projects, but it is not necessarily a negative phenomenon.
One way to respond to this situation is to ensure the quality and efficiency that has been emphasized
by this study’s participants and, in parallel, use the resistance as a driver to provide a method that
supports the automatic elicitation of requirements using continuous, relevant and available data, as
stated by the interviewees. This indication makes any theoretical argumentation on the benefits of a
method seem weak unless it includes an aspect of operationalization. This operationalization can be
complemented by a supporting tool. For this reason, the future steps of this project are the provision of
method and tool support for the elicitation of requirements using big data. The complementing tool
should be able to “mine” requirements using classification and facilitate the work of requirements
engineers.
6. Conclusions, and Future Work
In this study, we have applied a qualitative approach with semi-structured interviews and thematic
analysis to elicit the requirements for a method for the elicitation of system requirements from digital
data sources (big data). 16 requirements classified to the themes Method, Big Data, and Viewpoint, have
been elicited based on the codes generated during the thematic analysis. The main motivation for the
study was to report the insights of industry experts on the topic of data driven requirements elicitation.
The overall objective is to provide business organizations with a systematic aid for dealing with the
complexity of digital data management in relation to available and prioritized data sources, amount and
dynamics of the data in these sources, and different techniques for data processing and analysis. A
framework is intended to complement existing approaches to requirements elicitation.
Even a theoretical validity of the lack of a method has been reported in the survey [3], a limitation
of this study is that the elicited list of requirements might not be complete due to the given number of
interviews; however, the last two interviewees (I6, I7) belonged to companies where the study has been
performed over a period of several weeks and months [9] and by also discussing the topic with even
other workers. This led to a saturation of insights in relation to the data that was collected in the
interviews. Regardless of the possibility of being incomplete, the elicited requirements are considered
47
valuable and valid since they are derived from interviews with experts and practitioners working in
companies that are daily dealing with large amounts of user or machine-driven data related to their
software products. It is common that, in a DSR project, elicitation of requirements is done iteratively.
Additional requirements or refined requirements, may emerge in the subsequent phases of the project
when the initial version of the design artefact will be developed and validated.
The main direction for future work concerns elaboration of automation of the elicitation method, as
well as classifying the use of processing techniques and algorithms according to the specifics of data
sources and the software product to which they concern, and in accordance to that define reusable
capabilities for elicitation [14]. Another direction of interest is a further development of the framework
for model-driven engineering, to leveraging abstraction and automation in software development [15].
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