=Paper=
{{Paper
|id=Vol-2620/paper10
|storemode=property
|title=Importance of the Use of Analytics in Requirements Engineering
|pdfUrl=https://ceur-ws.org/Vol-2620/paper10.pdf
|volume=Vol-2620
|authors=Marina Pincuka
|dblpUrl=https://dblp.org/rec/conf/balt/Pincuka20
}}
==Importance of the Use of Analytics in Requirements Engineering==
https://ceur-ws.org/Vol-2620/paper10.pdf
Importance of the Use of Analytics in Requirements
Engineering
Marina Pincuka
Institute of Applied Computer Systems, Faculty of Computer Science and Information Tech-
nology, Riga Technical University, Kalku 1, LV – 1658, Riga, Latvia
marina.pincuka@rtu.lv
Abstract. Requirements Engineering is regarded as one of the most important
functions in software development process. Inadequate/ incorrect engineering of
requirements may lead to expensive errors in software development or even to
project failure. Even though there are a different methods and approaches that are
proposed in literatures, many of these approaches have not been used in the in-
dustry or have been proved to be ineffective. The main goal of this work is to
investigate the Requirements Engineering weak points and see which of these
weak points can be strengthened by the use of analytics.
Keywords: Requirements Engineering, Analytics, Requirements Engineering
Challenges
1 Introduction
In project, requirements definition is an important function that affects project pro-
cesses and results. Dissatisfaction of the requirements may lead to unhappy customers,
incorrect system processes or even project failure. The use of analytics allows to pro-
cess the information that otherwise may be ignored or overlooked.
Based on Dick J., Hull E. & Jackson K. [3], Requirements Engineering (RE) is the
subset of systems engineering concerned with discovering, developing, tracing, analys-
ing, qualifying, communicating and managing requirements that define the system at
successive levels of abstraction.
Dankov Y. and Birov D. [2] describes analytics as the process of developing action-
able insights through problem definition and the application of statistical models and
analysis against existing and/ or simulated future data.
The purpose of this paper is to reflect the results of the research in progress con-
cerning the importance of the use of analytics in Requirements Engineering functions.
Paper reports on three research questions: (i) “What is the state of art of the use of
analytics?” (ii) “What are the Requirements Engineering challenges?” and (iii) “How
the use of analytics in specified Requirements Engineering function can improve the
weak point of Requirements Engineering?”.
The paper is structured as follows. The survey of the state of art of the use of analytics
is presented in Section 2. Challenges in Requirements Engineering are presented in
Section 3. Summary of Requirements Engineering challenges and the use of analytics
Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0)
73
to reduce the Requirements Engineering weak points are described in Section 4. Brief
conclusions and directions of further research are stated in Section 5.
2 A Survey of the Use of Analytics
To understand the state of art of the use of analytics, a literature search was conducted
using terms “use” and “analytics”. 25 sources where selected and analysed using
Springer Link, Science Direct, IEEE resources, 13 of the sources, where identified as
overlapping and are not included in a review. Collected sources of types of analytics
are new and do not overlap with previous overview published in Pincuka and Kirikova
article “Types of Analytics in Requirements Engineering” [16]. New overview was
made to expand understanding of use of analytics in different fields.
The gathered articles, where analysed as follows: (i) “What is the industry in which
analytics are used?”, (ii) “What kinds of analytics are used?” and (iii) “What the ana-
lytics is used for?”. Gathered kinds of analytics and their brief definition is described
below:
1. Game analytics – applying analytics and big data in the gaming context [11];
2. Web analytics – the measurement, collection, analysis and reporting on Inter-
net data for the purposes of understanding and optimizing Web usage [1];
3. Visual analytics – information visualization that focuses in analytical reason-
ing facilitated by interactive visual interfaces [14];
4. Descriptive analytics –describes what is happening or why something hap-
pened [19];
5. Predictive analytics – provides foresight and make predictions about the like-
lihood of a future event [19];
6. Prescriptive analytics – provides support for making decisions, or some cases
independent form its own decisions [19];
7. Business analytics- comprised of solutions used to build analysis models and
simulations to create scenarios, understand realities and predict future states
[13];
8. Big Data analytics – the use of advanced analytic techniques against very large
diverse data sets that include structured, semi – structured and unstructured
data, from different sources and different sizes [13];
9. Diagnostic analytics – the form of advanced analytics that examines data or
content to answer the question, “Why did it happen?”. It is characterized by
techniques such as drill-down, data discovery, data mining and correlations
[5];
10. Text analytics – a process of converting unstructured text data into meaningful
data for analysis, to measure opinions, reviews, feedback, to provide search
facility, sentimental analysis and entity modelling to support fact based deci-
sion making [18];
11. Learning analytics - the measurement, collection, analysis and reporting of
data about learners and their contexts, for purposes of understanding and
optimizing learning and the environments in which it occurs [4].
74
Table 1 summarizes all identified types of analytics, industries in which analytics are
applied and usage of analytics in different use cases. These analytics are further used
in Section 4 in example to overcome Requirements Engineering challenges using ana-
lytics.
Table 1. Use of analytics in different industries
Type of analytics Industry Usage
Game analytics Information Technology: Understand use preferences and behaviour
Game development [11] [11];
Improve decision – making [11];
Gather insights [11];
Reduce the risk of failures [11]
Web analytics Information Technology [1]; Optimizing website functionality and con-
Website management [1]; version [1];
Marketing [1] Analysis of past performance [1];
Optimizing performance of and conver-
sions from marketing campaigns [1];
Determining the best creative executions
through testing [1];
Baseline information for site redesign [1];
Predictive metrics for developing future
marketing campaigns [1];
Budgeting and planning for upcoming
business objectives [1]
Visual analytics Medicine [14] Trend monitoring [14];
Anomaly detection [14];
Testable hypothesis detection [14]
Descriptive ana- Medicine [19]; Understand why something happened [19];
lytics Marketing [13] Provide information about past behaviour,
patterns or trends in the data [13];
Categorize, characterize, consolidate and
classify data to valuable information [15]
Predictive Medicine [19]; Prediction of the occurrence of future
analytics Information Technology events [19];
[13]; Supports decision – making [13];
Marketing [13]; Understanding of behavioural patterns and
Insurance companies [13]; trends [13]
Aviation companies [13]
Prescriptive ana- Medicine [19] Supports decision – making [19]
lytics
Business Marketing [13] Prediction of the future states [13];
analytics Understand of the reality [13]
Big Data Marketing [13]; Supports decision – making [13];
analytics
75
Type of analytics Industry Usage
Human Resource manage- Understanding of behavioural patterns and
ment [7]; trends [13];
Construction industry [15];Capturing the strategic linkage [7];
Information Technology [24]Improve performance [7];
Forecast future threats and opportunities
[24];
Enhance organizational performance [24]
Diagnostic Construction industry [15] Evaluation of the potential causes of a
analytics problem [15]
Text analytics Information Technology: Risk identification [8];
Mobile application develop- Benefit identification [8];
ment [8] Supports – decision making [18]
Learning analyt- Education [4] Understanding and optimization of pat-
ics terns [4]
The collection of types of analytics and the industries in which they are used allows to
(1) understand the main domains of application of analytics and (2) collect different
methods and approaches how analytics can be applied in different fields, to understand
how analytics can be applied in Requirements Engineering.
Gathered analytics are described without any classification into groups, because the
purpose of this article is to understand use of analytics in different fields and if it is
possible to apply this analytics in Requirements Engineering and overcome Require-
ments Engineering challenges.
3 Requirements Engineering Challenges
Requirements Engineering deals with a lot of challenges, for example, authors Kahan
et al. [9] mentions, previous identified requirements issues/ challenges like - business
process focus, systems transparency, integration focus, distributed requirements, layers
of requirements, packaged software, centrality of architecture, independent complexity
and fluidity of design. Author Schmid [22] mentions Global Requirements Engineering
challenges that are based on location of the stakeholders, in his article he mentions
challenges with International Requirements Engineering and Distributed Requirements
Engineering.
In this section Requirements Engineering challenges are collected based on Require-
ments Engineering phase in which challenge appears on.
Requirements Engineering function has a four phases [12]:
• Elicitation is the act to determine or obtain the relevant requirements for the
development of a solution. In this phase requirements are identified and dis-
covered;
• Requirements analysis is a phase, where a user’s requirements should be clar-
ified, categorized and documented to generate the corresponding specification.
76
In this phase requirements classification, representation, derivation and nego-
tiation are provided;
• Requirements specification describes the phase, where the requirements are
brought into a suitable and unambiguous form. The idea in this phase is to
document the requirements, and to make the requirements document readable
and understandable to anyone;
• Requirements validation is to review or validation requirements for clarity,
consistency and completeness. In this phase requirements faults are identified.
Table 2 presents challenges in Requirements Engineering and phases of Require-
ments Engineering in which challenges usually emerge.
Table 2. Challenges in Requirements Engineering
Requirements Engineering phase Challenges
Elicitation Incorrect understanding of the requirements [12][6];
System knowledge may be fragmentary, distributed and
tacit [21];
Lack of information [23][20][6];
Problems with client and customer representatives [23];
Problems in communication [23][10];
Conflicting requirements [20][10][6];
Random/ uncertain/ unclean requirements [20][10];
Unrealistic requirements [10];
Analysis Integrating physical objects with information objects
[21];
Change/ volatility of requirements [23][20][10][6];
Requirements quality issues[23];
Neglect of non-functional requirements [23];
Incomplete requirements [20];
Specification Minimal documentation [23][10];
Complexity of requirements documentation [10];
Validation Requirements validation[23];
Inadequate requirements verification [23];
Summarizing all of the challenges from different scientific papers (see Table 2.) the
conclusion can be made, that often authors emphasizes the same challenges in the same
phases of Requirements Engineering function.
4 Use of Analytics to Reduce Requirements Engineering
Challenges
In this Section using summarization of Requirements Engineering challenges are pro-
vided the types of analytics, which can be used to improve Requirements Engineering
issues. In table 3 are mentioned only those Requirements Engineering challenges, to
77
whom methods of analytics can be applied to, for example, analytics can not be used
with challenges in customer/ client representatives or minimal documentation, in these
cases other methods should be provided. Analytics, that can be applied to Requirements
Engineering challenges where identified in a literature survey, based on analytics use
cases and author master thesis Pincuka M. “Analytics in Requirements Engineering”
[17]. Analytics, which are proposed to use in Requirements Engineering challenges are
chosen based on use of analytics in a literature survey.
Table 3. Proposed types of analytics in Requirements Engineering challenges
Requirements Engineering challenge Types of analytics
Incorrect understanding of the re- Business analytics; Game analytics; Predictive ana-
quirements lytics;
Prescriptive analytics; Text analytics; Web analyt-
ics.
System knowledge may be fragmen- Big Data analytics; Descriptive analytics; Learning
tary, distributed and tacit analytics; Visual analytics
Lack of information Big Data analytics; Descriptive analytics; Text ana-
lytics
Conflicting requirements Descriptive analytics
Random/ uncertain/ unclean require- Descriptive analytics; Diagnostic analytics; Predic-
ments tive analytics, Prescriptive analytics
Unrealistic requirements Business analytics; Descriptive analytics; Diagnos-
tic analytics
Change/ volatility of requirements Big Data analytics; Descriptive analytics; Diagnos-
tic analytics; Predictive analytics, Prescriptive ana-
lytics; Text analytics, Web analytics
Requirements quality issues Business analytics; Descriptive analytics; Diagnos-
tic analytics
Neglect of non-functional require- Big Data analytics; Text analytics
ments
Incomplete requirements Descriptive analytics; Diagnostic analytics; Predic-
tive analytics, Prescriptive analytics
Types of analytics that are provided in Table 3, only points out some of the analytics
that can be used to improve or overcome Requirements Engineering challenges. For
example, (i) business analytics can be used to understand the reality, if we will apply
this analytics to Requirements Engineering it will help to better understand the require-
ment and it is meaning, (ii) if we identify conflicting requirements using descriptive
analytics data can be classified and meaningful information about requirements can be
found or (iii) using text analytics with keywords about the system we can identify some
additional information to reduce lack of information. To summarize all methods of an-
alytics that can be used to overcome Requirements Engineering challenges a literature
study must be provided with focus on analytics and use cases, where methods of ana-
lytics are used.
78
5 Conclusions
In this paper Requirements Engineering challenges are discussed and the use of analyt-
ics to overcome these challenges envisioned. The paper contributes (i) a preliminary
survey on the use of types of analytics in different industries, (ii) summary of Require-
ments Engineering challenges in Requirements Engineering phases and (iii) proposal
to the use specific types of analytics to overcome identified Requirements Engineering
challenges. Survey can be further researched collecting publications from different
years, industries, main topics and use cases.
Use of analytics in Requirements Engineering has a big potential, but nowadays use
of analytics in Requirements Engineering is still limited, the issues addressed in this
work are rarely surveyed, structured and organized, so the knowledge of the use of
analytics in Requirements Engineering could be reused and utilized effectively.
In previous research [16], first insights of use of analytics in Requirements Engi-
neering were collected and this research is a step towards the effective utilization of
different types of analytics in Requirements Engineering.
The presented research has several limitations – more sources of analytics and Re-
quirements Engineering challenges can be identified, analytics can be grouped by the
usage and use of analytics in Requirements Engineering challenges can be explained
using examples.
Nevertheless, the contribution of this research in progress provides insights about
possibilities of use of analytics in Requirements Engineering. The further research will
include overcoming of the above listed limitations.
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