=Paper=
{{Paper
|id=Vol-2218/paper3
|storemode=property
|title=Types of Analytics in Requirements Engineering
|pdfUrl=https://ceur-ws.org/Vol-2218/paper3.pdf
|volume=Vol-2218
|authors=Marina Pincuka,Marite Kirikova
|dblpUrl=https://dblp.org/rec/conf/bir/PincukaK18
}}
==Types of Analytics in Requirements Engineering==
https://ceur-ws.org/Vol-2218/paper3.pdf
Types of Analytics in Requirements Engineering
[0000-0002-1678-9523]
Marina Pincuka and Marite Kirikova
Institute of Applied Computer Systems, Faculty of Computer Science and Information
Technology, Riga Technical University, Kaļķu 1, LV-1658, Riga, Latvia
marina.pincuka@rtu.lv, marite.kirikova@rtu.lv
Abstract. Different methods of analytics are popular in various areas of human
activities. This also refers to requirements engineering. However, the number of
research works on the usage of analytics in requirements engineering is still
limited; and the issues addressed in these works are rarely surveyed, structured
and organized so that the knowledge of the use of analytics in requirements
engineering could be reused and utilized effectively. This paper contains a
preliminary survey of types of analytics used in requirements engineering and a
mapping of the types of analytics onto the continuous requirements engineering
framework. This work is a step towards the effective use of analytics in
requirements engineering. The survey of the types of analytics points to the
sources of existing knowledge of the use of analytics in requirements
engineering. The mapping of the types of analytics onto the requirements
engineering framework depicts the sources of data that can be used in different
types of analytics.
Keywords: Requirements Engineering, Analytics, Data Analysis.
1 Introduction
Analytics is the application of computer systems to the analysis of large data sets for
the support of decisions in a particular domain [1]. The use of analytics gives an
opportunity to utilize information which is hard or impossible to handle manually.
Today different types of analytics are applied in almost all areas of human and
machine activity, including requirements engineering.
The purpose of this paper is to reflect the preliminary results of the research in
progress concerning the use of analytics in requirements engineering. It reports on
two research questions: (i) “What is the state of art of the use of analytics in
requirements engineering?” and (ii) “What are the main sources of data to be used in
different types of analytics in requirements engineering?”,
A survey of related works in requirements engineering analytics was conducted to
answer the first research question. Further, in order to have a structured view on the
sources of data used by different types of analytics, a mapping of the types of
analytics onto the requirements engineering framework was performed. Thus, the
paper contributes a survey of analytics methods used in requirements engineering and
provides a structured view on the sources of data that can be utilized by different
types of analytics in requirements engineering. It must be noted, that the results refer
to currently available sources of literature. Due to popularity of analytics applications
and fast development of the field, it is important to take into account that the survey
will have to be updated at least once per year until the time when the area of analytics
application in requirements engineering will reach a higher level of maturity.
The paper is structured as follows. The survey of types of analytics used in
requirements engineering is presented and discussed in Section 2. The mapping of the
types of analytics (identified in Section 2) onto a requirements engineering framework
is demonstrated in Section 3. Brief conclusions and directions of further research are
stated in Section 4.
2 A Survey of Types of Analytics used in Requirements
Engineering
To get a preliminary view on the state of art in the use of analytics in requirements
engineering, a literature search was conducted using terms “analytics” AND
“requirements engineering”. Nine relevant sources were selected using IEEE,
Springer, ACM, and Science Direct resources. The gathered sources were analysed as
follows. First, it was identified which kinds of analytics are used in each source for
what purposes. Second, the identified usages were grouped by identified kinds of
analytics. The gathered kinds of analytics and their brief definitions are described
below:
Advanced analytics – focuses on forecasting future events and behaviours,
allowing businesses to conduct what-if analysis to predict the effects of potential
changes in business strategies [2]. This kind of analytics uses historical data to
analyse alternative actions.
Big Data analytics – Big data analytics examines large amounts of data to
uncover hidden patterns, correlations and other insights [3], [4].
Descriptive analytics – the field of study were a (typically large) dataset is
described quantitatively on its main features with the aim to reduce the amount of
data into ‘human consumable information’ [5], [6]. Descriptive analytics
provides insights about the past and current business performance [7].
News analytics – the measurement of the various qualitative and quantitative
attributes of textual (unstructured data) news stories [8].
Predictive analytics – the field of study where a prediction is made about the
future based on information from the past and current situations [5].
Prescriptive analytics – the field of study in which the actions are determined that
are required to achieve the goal. Prescriptive analytics only determines what will
happen if we continue the current trend of activities [5]. It usually works with
structured data [5].
Text analytics – involves information retrieval, lexical analysis to study word
frequency distributions, pattern recognition, tagging/annotation, information
extraction, data mining techniques including link and association analysis,
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visualization and predictive analytics [8]. Here usually unstructured data is used
for the analysis.
Visual analytics - creates a path (from data to decisions) that enables the decision
makers to extract insights by interacting with the relevant information [9]. The
use of unstructured data to represent it in understandable visualizations is
discussed in [10].
Basing on the analysis of the use of different types of analytics that were gathered
during systemic literature review and are listed above, a grouping of data used in
analytics is proposed. This grouping helps to understand which types of analytics can
be used and applied in which situations of requirements engineering. The following
four groups of data where revealed:
Unstructured data – analytics works with unstructured data.
Large datasets – analytics works with datasets that usually are large.
Historical data – analytics works with past information.
Structured data – analytics works with structured data.
The groups of data are not mutually exclusive. They just point to the main data
variations addressed in current researches that report on the use of analytics in
requirements engineering. Based on the groups of data, the analyzed analytics
methods were organized in 8 overlapping types, which together with the examples of
their application are reflected in Table 1. These types of analytics are further used for
the mapping of the analytics methods onto the requirements engineering framework in
Section 3.
Table 1. Analytics in requirements engineering
Types of analytics Application examples in the related work
Advanced historical data analytics Future prediction [2]
Big Data large datasets analytics Understanding customers needs,
Understanding and optimization of business
processes, Performance optimization [3]
Descriptive large datasets analytics Requirements analysis, History analysis [11]
News unstructured data analytics Measure qualitative and quantitative
datasets [8]
Predictive historical data analytics Risk mitigation, Future prediction [11]
Prescriptive structured data Future prediction [11]
analytics
Text unstructured data analytics Pattern recognition, Annotation, Information
extraction,
Future prediction [8]
Visual unstructured data analytics Requirements analysis, Requirements
visualization,
Displaying Data [9]
The related works discussed in this section reveal that the use of analytics in
requirements engineering can help to identify the requirements which otherwise might
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be overlooked or un-recognized. For instance, in large software systems (system size,
functionality breadth, component maturity, supplier heterogeneity), it is advisable to
apply software repository mining for understanding, evaluating, and predicting the
development, management, and economics of such systems [12]. The use of analytics
can also help to correct wrong requirements. For instance, by the use of visual
analytics, with the dashboard and sketching systems interface [10] we can capture
issues like a field with incorrect input data or button with wrong functionality.
Analytics can also help to improve the quality of collected requirements, for instance,
by analysing large datasets from similar (e.g. open source) software systems [3]; all
common functions can be collected and, based on them, the missing system
functionalities retrieved.
At the current stage of research, the overlapping types of analytics and overlapping
groups of data sources; and continuous emergence of new analytics approaches are a
challenge for designing a method for the choice of particular types of analytics for
particular requirements engineering cases. To overcome this challenge, it was decided
to design a method that helps to gradually amalgamate the acquired knowledge so that
it might be extended and refined; and, simultaneously, already now, could be put in
the practical use to arrive at generic recommendations and methods with respect to the
choice of the types of analytics in particular cases of requirements engineering. The
proposed approach is discussed in the next section.
3 Data Sources Based Mapping of Types of Analytics onto
Continuous Requirements Engineering Framework
To understand how analytics can be used in requirements engineering, we have
designed a simple method of mapping the known types of analytics onto the
requirements engineering framework. The method consists of the following five steps
(the first three steps refer to the representation of the existing knowledge about the
use of analytics in requirements engineering; the last two steps refer to the use of this
knowledge in actual requirements engineering cases):
1. Choose the requirements engineering framework, such that its components
would allow distinguishing between analytics data sources.
2. Map visually each type of the analytics onto the framework so that it would be
visible which component of the framework provides data for which type of
analytics.
3. Repeat the second step whenever the new type of analytics or the new data
source for the already mapped types of analytics is detected in the related
work.
4. For a specific case of requirements engineering, identify possible sources of
data for particular types of analytics.
5. Apply those types of analytics where data are available, if these types of
analytics can provide useful knowledge in the requirements engineering
process.
The choice of a particular framework onto which to map the types of analytics
depends on the preferences of users. Each framework that satisfies the conditions
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mentioned in the first step of the method is applicable. We have chosen the
FREEDOM framework that was developed for the purposes of continuous
requirements engineering and is reflected in Fig. 1 [13], [14]. The framework is
suitable for the method because of the following reasons:
It gives an opportunity to distinguish between different data sources.
It gives an opportunity to distinguish between internal and external data
sources with respect to the enterprise or project where the requirements
engineering is applied.
It has a fractal nature [14], i.e. requirements engineering, on a smaller scale, is
a constituent of each component of the FREEDOM framework.
The framework itself prescribes the usage of monitoring, auditing and
analytics (maa links in Fig. 1).
The FREEDOM framework has the following constituents-functions (see Fig. 1):
F– Future representation, R – Reality representation, E1 – requirements Engineering,
E2 – fulfillment Engineering, D – Design and implementation, O – Operations, and
M– Management.
Fig. 1. An example of mapping between the types of analytics and the requirements
engineering framework.
F – Future representation is the constituent of the framework that is responsible for
representation of the To-Be situation. In Fig. 1 the sources of data in this function are
represented with (6).
R – Reality representation is responsible for all artifacts that represent the present
(As-Is) situation. The sources of data in this function are represented with (5)
E1 – requirements Engineering is the function dedicated to the model and tool
based acquisition and management of high quality requirements. The sources of data
in this function are represented with (1).
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E2 – fulfillment Engineering is the function that takes care of handling project
portfolios that would lead to the fulfillment of stated requirements. The sources of
data in this function are represented with (2).
D – Design and implementation is the function that produces the design and
handles implementation of the target system. The sources of data in this function are
represented with (3).
O – Operations regard the actual operation of the implemented system, including
its maintenance. The sources of data in this function are represented with (4).
M – Management refers to all levels of management under which the target system
operates [13]. There is number (8) that refers to data in management function,
however, it has to be taken into account that in Management function information
linkages differ from those of other functions of the framework.
Number (7) is assigned to data sources external to the enterprise. This data can be
used for Big Data analytics, news analytics and text analytics that refer to the external
data sources (7) in Fig. 1 [3], [15]. Numbers (1) to (6) refer to internal enterprise data.
As it is shown in Fig. 1 and in Table 1, the analytics of one and the same type can
use data from several functions (components) of the framework. For instance,
prescriptive analytics helps organizations make better decisions by optimizing trade-
offs between business goals, such as costs or customer service, while considering
predictions, rules, and constraints on available resources, to recommend the best
course of action; whether decisions are made on a configuration, design, plan, or a
schedule [5]. Thus, the prescriptive analytics can be used not only for the data (6) of
Future representation (analyzing data collected in past to predict the future changes),
but also for data (1) in requirements Engineering component of the FREEDOM
framework (applying analytics to the data of previous requirements gathering cases
can help to improve requirements of systems in future).
Predictive analytics has a lot of capabilities - visualization, modeling, data mining
management and deployment [5], [7], so predictive analytics can be used in (6) Future
representation (analyzing past events to predict changes in future); in (1) requirements
Engineering (gathering new requirements while analyzing existing systems); in (3)
design and implementation (achieving the most effective development alternatives
while modeling each development phase); and in operations and management
component (applying present and future analytics based on historical data). Assuming
that prescriptive analytics and predictive analytics both are advanced analytics
technologies [2], we can make a conclusion that advanced analytics can be used in all
functions (components) of the FREEDOM framework that are mapped to predictive
and prescriptive analytics. Descriptive analytics can be a preliminary stage of data
processing that creates a summary of historical data to yield useful information and
possibly prepare the data for further analysis [2], [6]. Descriptive analytics can be
used to represent both: the past and the reality, and to use collected information in
requirements engineering. The basic idea of visual analytics is to visually represent
the data so as to allow the employees to directly interact with the information [10],
[16]. It means that visual analytics can be used not only for data sources (1) in the
requirements Engineering function/component (using dashboards to draw sketches),
30
but also, for instance, in data sources (3) in the Design and implementation function
(e.g. making charts with phase goals, to keep the track of development progress).
The mapping shown in Fig. 1 gives an opportunity to see where from the data can
be taken in the process of requirements engineering and what types of analytics can
be applied to this data. Such representation helps to make decisions about what data
analytics might be useful and possible in particular requirements engineering
situations.
4 Conclusions
In this paper we discussed the usage of analytics in requirements engineering. The
paper contributes (i) a preliminary survey on the use of different types of analytics in
requirements engineering, (ii) a mapping of analytics types onto the requirements
engineering framework that helps to visualize and amalgamate the knowledge on the
use of analytics in requirements engineering.
The presented research has several limitations (i) the surveyed types of analytics
overlap, (ii) also the identified sources of data, used in analytics, overlap, (iii) this is
only a preliminary survey – more sources might be identified with more sophisticated
search methods, and (iv) the proposed method of mapping the types of analytics onto
the requirements engineering framework is manual, and thus has a restricted
representation flexibility.
Nevertheless, the contribution of this research in progress is a step towards the
effective utilization of different types of analytics in requirements engineering. The
further research will include overcoming of the above listed limitations and
developing the software tool that supports the effective use of requirements
engineering analytics.
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