Download integrating ai techniques in sdlc: requirements phase perspective

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Shreta Sharma et al, Int.J.Computer Technology & Applications,Vol 5 (3),1362-1368
INTEGRATING AI TECHNIQUES IN SDLC: REQUIREMENTS
PHASE PERSPECTIVE
Shreta Sharma
St. Xavier’s College, JAIPUR - 302001, INDIA
[email protected]
S. K. Pandey
Board of Studies, The Institute of Chartered,
Accountants of India(Set up by an Act of Parliament)
NOIDA – 201309, INDIA
[email protected]
Abstract: Software Development Life Cycle (SDLC) refers
to a process of describing the planning, designing, coding,
and testing of a software system as well as the way, in which
these activities are implemented. Undoubtedly, requirements
phase is the foundation stone of the entire development life
cycle. Requirements phase has various stages such as
elicitation, specification, validation, management and
documentations, which aim to collect good requirements
from stakeholders in the right way, but at the same time,
there exists various related challenges too. One of the
foremost issues is the maximum human intervention in the
requirements phase of SDLC. Some research studies reveal
that Artificial Intelligence (AI) techniques may help in this
regard by minimizing human intervention by offering several
tools/ techniques to automate certain processes up to a certain
extent. In this paper, our aim is to identify the issues in each
of the stages of the requirements phase and possibility of AI
techniques to overcome these identified issues. In addition,
the paper also explores the relationship between these issues
and their possible AI solution/s through Venn-Diagram. For
some of the issues, there exist more than one AI technique
but for various issues, no AI technique/s have been found to
overcome the same and accordingly, those issues are still
open for further research.
Keywords: Software Development Life Cycle, Artificial
Intelligence, Requirements Phase, Artificial Intelligence
Techniques and Requirements.
I.
INTRODUCTION
Requirements phase is the foremost phase in the SDLC,
which refers to the process of producing, estimating,
specifying, associating and changing the objectives,
functionalities, qualities and constraints to be achieved by a
software system [1]. Quality of requirements is one of the
factors responsible for the failure and success of any software
[1]. Research studies reveal that success in 68% of
technology projects is improbable. Poor requirements cause
many of these failures; it implies that projects are condemned
right from the beginning. As a result of this, now-a-days,
software development companies are devoting more time and
budget in requirements phase on their projects. Because, it is
also widely noted that requirements errors are the most
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expensive to correct late in the development process or when
the product is released[2].
For managing failures and challenges of software systems
due to poor requirements in software development, there has
been a visible growth of related disciplines [3]. Integrating
AI techniques in requirements phase is having a great impact
on SDLC. Artificial Intelligence is a field of computer
science usage, which attempts to build computational
methods for actions that are measured to require intelligence
when performed by humans [1][4]. It produces ontologies,
pattern recognition, creativity, solving problems, learning,
stimulation, deduction, classification, building logic, and
knowledge representation. It is concerned with the study and
creation of computer systems that display some form of
intelligence and efforts to apply such knowledge and
techniques to the design of methods and computer based
programs that can comprehend a natural language or human
intelligence [5].
In software development, requirements phase is considered
as a phase having ample scope to incorporate AI techniques
because of requirements’ environments. However,
requirements tends to be vague, incomplete and confusing
and have a big impact in all the development stages [1][6].
Therefore, the usage of AI techniques in order to improve
requirements phase favorably affects the quality of overall
software life cycle [1][7].
This paper aims to study the techniques developed in AI
from the standpoint of their applications in requirements
phase. In particular, it focuses on techniques developed or
that are being developed (under conceptual stage/s) of AI that
can be arranged in solving issues associated with
requirements phase of SDLC. The rest of the paper is
organized as follows: Section II describes ‘RE Process and
its Key Issues’ whereas Section III provides a detailed
discussion on ‘AI Techniques in Requirements Phase’.
Section IV presents ‘Related Discussion and Findings’ and
finally ‘Conclusion and Future Work’ are reported in Section
V.
II.
RE PROCESS AND ITS KEY ISSUES
Requirements Engineering (RE) is one of the most important
phases of software development. It is seen as a collection of
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well-defined activities, techniques, and transformations that
people use to develop requirements of a system, maintain the
requirements specification and associated artifacts. Good
quality RE process is therefore essential for successful
system development. RE process has several phases, which
facilitate to understand the customers’ requirements, define
the system constraints, evaluate them, estimate their
feasibility, determine actual need of customers, validate
requirements specification and manage the requirements
[8][9]. Basically, there are five major phases of RE, which
have been covered in the following sub sections:
2.1 ELICITATION
It is regarded as a critical activity in the requirement
development process; it explores the requirements of
stakeholders and is normally considered as the process of
detecting the real needs of the customers as well as the
system [8][9].A detailed study reported various techniques
for elicitation process such as: Traditional techniques,
collaborative technique, contextual, cognitive and innovative
techniques; however it has been observed that there are still
certain issues as well as challenges in elicitation, which are
given as follows:
 EI1: In a survey, sometimes interviewers ask some
questions but don’t get response according to his/her
requirements. In normal interaction, these issues of
explanation are exchanged between participants [9].
 EI2: In questionnaires method, which is used in
requirement elicitation, when questions are asked from
different stakeholders, they are not assured by having
same words repeated in each session related to subject.
These words will impact different meanings to different
people in different environment; this is a social context
[9].
 EI3: Some statement of conversational method between
interviewer and stakeholders are quite challenging and
create doubt using this method for some area[9].
 EI4: Furthermore, because stakeholders may have
widely different status within the organization,
requirements engineers will face difficulty to share their
thoughts, specifically if idea is not very widespread [9].
 EI5: Time limitation and sensitivity are the major issues
of contextual technique where project has tight schedule
along with time limitation at requirements stage and not
enough time for observation of requirements [9].
2.2 ANALYSIS
Requirements Analysis is a process during which
requirements are analyzed and modeled. It involves a set of
activities, which aim to discover problems within the system
requirements and achieve agreement on conflicts by
satisfying all system stakeholders. If an analyst discovers
some problems with the requirements during the analysis
phase, such requirements are referred back to the elicitation
phase. This process is related to the requirements that are
incomplete, ambiguous and/or conflicting [8][9].Major issues
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reported by experts and practitioners in requirements analysis
are given as follows:
 AnaI1:Stakeholders will be unable to communicate
embedded knowledge. Additionally, group facilitators
try to abstain stately their own categories to stakeholders
but there is less surety that the stakeholders will actually
share those categories in meetings .
 AnaI2:Requirements are generally written in natural
language, which is hard to handle with quality.
 AnaI3: Changes in requirements occur frequently during
the course of project.
 AnaI4:Users are technically unsophisticated and do not
understand the development process.
 AnaI5:Customers have unreasonably timelines.
2.3 DOCUMENTATION
Requirements documentation is the process by which agreed
requirements are achieved at an appropriate level of detail in
the most suitable notation, based on a well-defined document
structure. The documentation process receives its input from
the analysis and negotiation process[8][9]. The output of the
process is a well-structured and defined specification. During
this process, the team organizes the requirements in such a
way that ascertains their clarity, consistency, traceability etc.
Major issues reported by experts and practitioners in
requirements documentation are given as follows:
 DI1: The key issue in the documentation process is to
select proper notations according to document
requirements and at the appropriate level of details.
 DI2: To elicit ‘how experts structure their knowledge
about a domain’, card sorting and repertory grid provide
ways to elicit attributes that are not immediately and
easily expressed by the experts. It means that it is
difficult for an expert to communicate attributes of this
method [9].
 DI3: Some issues such as Expensive, time-consuming
and cost of the documentation may exceed its value.
 DI4: In previous literature, it has been also found that
documentation suffers from unavailability and
maintainability of documents.
2.4 VALIDATION
Requirements validation process check the requirements
document to ensure that it is unambiguous, consistent and
complete, and confirm that models and documentation
accurately express the stakeholders’ needs along with final
requirements specification. This activity also includes
validating the system requirements against raw requirements
and verifying the correctness of system requirement
documentation[8][9].Major issues reported by experts and
practitioners in requirements validation are given as follows:
 VI1: Statements given in the documents conflict with
other statements about the same topic in the similar
document or in the equivalent validation package.
 VI2: One of the major problems in requirements
validation phase is the failure of essential information.
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

VI3: The requirements in the validation package did not
effectively express the needed information to the
software.
VI4: In addition, other major problem in validation phase
is traceability of requirements. In some cases traceability
matrix does not work efficiently.
2.5 MANAGEMENT
Requirements management is the process of identifying,
organizing, documenting and tracking changing requirements
in a project and then monitoring the needs of all stakeholders
involved in a project. It is an ongoing task throughout the
whole RE process and might extent the whole software
lifecycle. Some of the most important maintenance tasks
during this phase include the updating of the requirements as
well as the degree of evolution support that the approach
provides[8][9]. Major issues reported by experts and
practitioners in requirements management are given as
follows:
 MI1:Major issue in requirement management is incorrect
and missing links between requirements, which drain the
quality of software.
 MI2:Impact of a requirement change on other
requirements and use cases is also a key issue.
 MI3:Getting project stats (requirements status and
counts, project progress per phase etc.) is the third major
issue.
 MI4: Duplication of requirements between different
documents is also a major issue of requirements
management.
III.AI TECHNIQUES IN REQUIREMENTS PHASE
The main aim of requirements phase is to gather and analyze
requirements and to transform it into crystal clear
representation. But, some issues in this phase such as
incompleteness, ambiguity and misunderstanding of
requirements decrease the quality of software [10][11][12].
Therefore some AI methods and techniques with respect to
affirmative issues that are identified in section II of this paper
may be used to overcome the same.
3.1 AI Techniques to Overcome the Issues of Elicitation
In accordance with the issues related to elicitation
techniques, highlighted above, the paper now proceeds to
explain the AI techniques, which are given as follows:
 AI (E1):Ontology: One of the major goals of
requirements elicitation is to achieve a common
understanding between all project stakeholders regarding
the set of requirements. Simultaneously it faces some
challenges and limitations too such as incompleteness of
requirements, conflict analysis and tracing of huge
requirements[12] [13]. The use of semantic technologies
seems promising for addressing these challenges.
Ontology is an explicit specification of a
conceptualization and it provides the means for
describing the concepts of a domain and the
relationships between these concepts in a way that
allows automated reasoning. This technique is used for
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



the semantic web and provides solutions for
representing, organizing and reasoning over the complex
sets of requirements knowledge and information, which
helps to enhance the quality of software life cycle[14]
[15].
AI(E2):Natural Language Processing: The fundamental
issues of natural language are Lexical ambiguity,
alternative parts of speech (according to circumstances),
and word class (adjective, article, preposition, number,
noun, pronoun, adverb, verb, determiner etc.) along with
Syntactic ambiguity due to the complexity of sentence
structure. These issues result in decline of productivity
and quality of the software life cycle [16]. Over the
years, various researchers have conducted study in this
area and found a concept to use Natural Language
Processing (NLP) in the elicitation of requirements,
specifically for removing ambiguity and incompleteness
of requirements. The previous studies proposed that
developing an ontology between two categories
(incomplete, ambiguity) and application of ruled based
algorithm might help to address the issue and might also
offer a better understanding of problems for stakeholders
[17].
AI(E3):Keyword Mapping: Many system development
failures occur because the stakeholders cannot describe
their requirements correctly, or developers and domain
experts neglect “observable” words that contribute
basically to system requirements. These challenges can
be avoided by mapping each keyword spoken by each
stakeholder. The previous studies introduced a keyword
mapping technique for developers so that they can
identify keywords used by stockholders to support them
in preparing formal requirements. This technique
provides a base to collect information about user’s need
and stakeholder’s requirements and expectations at the
initial stage by organizing interviews along with
descriptions. System experts and domain experts
evaluate recorded interview session based on keywords
for avoiding ambiguity in requirements specifications.
The process is basically useful for eliciting social
requirements during software development process [18].
AI(E4): Speech Understanding Methodology: One
aspect of system qualities is the ability to "listen in" on a
discussion and properly capture these statements into a
single vision. The approach is proposed to develop
elicitation methods in which stakeholders are more
openly asked to express their requirements. AI speech
research generally uses a context-free grammar to
discover and improve correctness, divide the
stakeholders' utterances and finally to categorize the
known statements by quality form [19].
AI(E5):Sketch Based Modeling: Requirement engineers
and stakeholders prefer to sketch requirements during
early elicitation phases in order to communicate ideas
and to make them constant. Whiteboards, as well as pen
and paper, are still the dominant tools that are used over
current software modeling tools. Further, to facilitate
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requirements management, engineers need to manually
convert the sketches into more formal models of
requirements. This is a tedious and time-consuming
work. Additionally, there is a risk that the original
intentions of the sketched models and informal
explanation get lost in the transition, therefore, while
managing these issues, various researchers worked in
this field and proposed a concept for a flawless, toolsupported transition from informal, sketchy drafts to
more formal models by providing sketch based modeling
tools such as UML diagrams[18][19].
3.2 AI Techniques to Overcome the Issues of Analysis
In this section, the possibility of incorporating AI techniques
to overcome the issues relating to requirements analysis has
been explored. These techniques are given as follows:
 AI(Ana1):Neural Network: In requirements analysis,
major focus is typically the discovery and prediction of
the key requirements issues initially and then analyzing
them individually. To make this task easy, there is a
promising tool of AI called neural networks, which is
used for problems that require classification given some
predictive input features. They therefore appear ideal for
situations in Software Engineering where one desires to
predict outcomes, such as the software risks associated
with modules and risk analysis in software maintenance.
[14][18][19].
 AI(Ana2):Lightweight
Semantic
Processing
Approach: One of the most crucial problems to
automate requirements analysis is that requirements
documents are usually written in natural language.
Although techniques for Natural Language Processing
(NLP) are being advanced now-a-days, it is hard to
handle such requirements documents sufficiently by
computer. However, lightweight semantic processing in
requirements is essential for producing requirements
specifications of high quality. This approach establishes
a mapping between requirements specification and
ontological elements. This technique allows us to have
the possibility of automating semantic analysis with
lightweight processing by mapping requirements
descriptions in a requirements document onto
ontological elements [20].
 AI(Ana3):Knowledge Based System: A key problem in
requirements analysis is to resolve and track the
dependencies between an characteristics of related
systems and the envisioned system's requirements.
Knowledge engineering is a subfield of AI that produces
a type of computer system called knowledge-based
systems also known as expert systems to ensure the
quality of software. Knowledge-based systems are
computer programs designed to perform tasks usually
done by human experts, or to solve problems that are
beyond the capability of conventional computer systems.
Knowledge based system process includes rule based
reasoning and frame based model [21].
3.3 AI Techniques to Overcome the Issues of
Documentation
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AI techniques, which may be helpful to resolve the issues of
requirements documentation, are given as follows:
 AI(D1):Nature Prototyping: Requirements phase in
particular is characterized by its highly creative nature,
making their process difficult to express. This has two
main consequences. Firstly, the ways-of-working on the
method cannot be expressed precisely enough by using
an algorithm-like planning approach. Secondly, even
though CASE tools are efficient in recording, retrieving
and manipulating system specifications but still, they fail
in actually supporting the developers in proceeding in
the development. The NATURE way of tackling these
two interrelated problems is to concentrate on the
development process itself and to propose a generic
framework that is powerful enough to allow the building
of knowledge bases containing precise ways-of-working
definitions and underlying more helpful CASE tools
[22].
3.4 AI Techniques to Overcome the Issues of Validation
Major AI techniques, which may be used to overcome the
issues relating to requirements validation, are given as
follows:
 AI (V1):KBS Validation: Validation is an important
procedure in the entire Knowledge Based System (KBS)
life cycle. A knowledge base integrated into such
systems has to be verified or validated [23]. The earliest
validation technique in AI was Turing test, which was
based on how to decide if a program could be considered
intelligent. Although many criticisms have been leveled
against the Turing test as a general procedure to
characterize intelligent behavior but the idea of this
testing has remained central in KBS validation [24].
There have been many approaches and procedures to
develop KBS validation such as knowledge validation
mappings, formal specification techniques and empirical
evaluation, aimed at assuring the highest level of
knowledge quality deals with practical guidelines of
knowledge validation [23].
 AI (V2): Cross Validation: Cross validation is a model
assessment method that is more efficient than previous
formal method. The problem with previous evaluations
is that they do not give a suggestion of ‘how fine the
learner will do when it is asked to build new predictions
for data it has not already seen’ [23]. The small amount
of the data is removed before training start. When
training is over, the data that was detached can be used
to check the performance of the learned model on
innovative data.
 AI (V3):Viewpoint Resolution: A specific techniqueviewpoint resolution is a idea of providing early
validation of the requirements for a multifaceted system
and some beginning experimental details of the
efficiency of a semi-automated execution of the
technique are provided. The technique is based on the
fact that software requirements can be elicited from
diverse viewpoints, and that evaluation of the differences
resulting from them can be used as a approach of support
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in the early validation of requirements. This study of
views is able of differentiating between lost information
and contradictory information, thus providing support
for viewpoint resolution [23].
 AI (V4):Model-Based Automated Validation: Modelbased approaches can develop quality and decrease cycle
time by simulating the models to achieve early
validation of requirements. It is a systematic approach
for analyzing, documenting and validating the system
requirements. Additionally, information that provides
resources of expressing requirements is also related with
the method. There is no single perfect requirement
method available but an array of modeling techniques
are used to prepare the system requirements. Literature
reveals that there are several modeling techniques, which
are used for the validation of the requirements such as
data flow modeling, compositional models, simulation
models and model based test process[23].
3.5 AI Techniques to Overcome the Issues of
Management
Major AI techniques, which may provide significant help in
requirements management, are given as follows:
 AI (M1):Knowledge Based Systems for Management:
Knowledge-based system supports to developers to
ensure the quality of software requirements. Framebased model is proposed as the most appropriate for
knowledge organization in the system and its structure is
derived from textual sources analysis and experts
interviews. Application of concept mining methods with
external domain or common sense ontologies or
vocabularies serving as thesaurus is proposed, to make
possible
intellectualized
quality assurance
of
requirements by the KBS [24].
IV. RELETED DISSCUSSION AND FINDINGS
In this section, it is identified that for each issue relating to
every stage of RE, how many AI technique/s are available in
the literature, which can be used to overcome the same. This
is represented with the help of Venn-Diagram. The
relationship e.g. 1-> 1 and 1-> many has been identified
between the issues and possible AI technique/s. The same is
given as follows:
 Based on the facts, presented in Section II and III,
mapping is drawn with reference to elicitation issues and
AI techniques, which is given in Fig. 4.1. It is evident
from the mapping that for each issue except EI 5, at least
one AI technique is available in the literature. However,
for EI5, no AI technique/s has been reported yet. Hence,
there appears ample scope for the researchers to conduct
further work in the area.
Fig. 4.1: Mapping of Elicitation Issues

Based on the related facts given in Section II and III,
mapping is drawn in Fig. 4.2. The diagram demonstrates
the links between issues and AI techniques related to
analysis stage. It is evident from the mapping that for
each issue, at least one AI technique is available in the
literature. However, further work may be initiated to
enhance the performance of already available AI
techniques to provide more fruitful results.
Fig. 4.2: Mapping of RE-Analysis Issues
 Keeping in view the related aspects, mapping is drawn
with reference to documentation issues and AI
techniques, which is given in Fig. 4.3. It is evident from
the mapping that only for two issues DI1, and DI2, one
AI technique is available in the literature. However, for
remaining two issues DI3, and DI4, no AI technique/s
exists in the literature. Hence, there appears good scope
for further work in the area.
Fig. 4.3: Mapping of RE-Documentation Issues

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In the similar lines, mapping is also drawn with
reference to RE-Validation issues and AI techniques,
which is given in Fig. 4.4. It is evident from the mapping
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that for all the issues except VI4, at least one AI
technique is available in the literature. However, for VI4,
no AI technique/s exists in the literature. Hence, one of
the future tasks may be initiated on the same to fill-up
the gap in between.
Fig. 4.4: Mapping of RE-Validation Issues

Finally, mapping is also drawn with reference to REManagement issues and AI techniques, which is given in
Fig. 4.5. It is evident from the mapping that AI
technique exists only for MI1. For remaining three issues
viz. MI2, MI3 and MI4, no AI technique/s exists in the
literature. Hence, the researchers may work in these
directions and offer an appropriate solution.
Fig. 4.5: Mapping of RE-Management Issues
V. CONCLUSION AND FUTURE WORK
Research studies reveal that AI techniques have a great
impact on requirements phase of SDLC. In the current
scenario, the demand to formulate a framework, based on
integration of AI techniques and methods, ontologies,
knowledge based matrices and others techniques/tools has
increased dramatically, which is raising many new research
questions. Accordingly, the paper presented various
techniques developed in AI to eliminate the issues such as
inconsistency, ambiguous and redundancy of requirements
phase. The paper also described significant use of AI
techniques in each stage of requirement phase. In addition,
proper mapping of the issues belonging to each of the RE
stages with the relevant AI techniques has also been
accomplished with the help of Venn-Diagram. These
diagrams present a clear understanding of the current status
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of the research in the area along with the scope for future
research. Any researcher, who wishes to work in the area,
can directly take these issues as his/her research problem/s
and start working on the same.
As stated above, future task may be to work on these
unresolved issues and present some appropriate AI
techniques to overcome the same. In addition, apart from the
existing AI techniques, some more techniques may also be
explored to improve the efficiency of the existing techniques
in the light of new technological advancements in the area.
Work may also be initiated for other phases of SDLC in the
similar lines with an objective to integrate AI techniques in
each of the generic phases of the SDLC. Such proposals are
expected to be found useful for the software companies as
well as research community up to a great extent.
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Shreta Sharma is currently working as a
Faculty in the Department of Computer
Science, St. Xavier's College, Jaipur.
Prior to this, she was associated with
Natural Softwares Pvt.Ltd., Jaipur as a
Software Developer. She has an
excellent academic background right
from the school level. Under the
Institute-Industry linkage program, she delivers expert
lectures on various area of Computer Science. She has
contributed many research papers in the conferences of
International/ National repute. Her area of research includes
Artificial Intelligence, Requirements Engineering, E-learning
and Software Security.
Dr. Santosh K. Pandey is presently
working as a Faculty of Information
Technology with Board of Studies, The
Institute of Chartered Accountants of India
(Set up by an Act of Parliament) New
Delhi. Prior to this, he worked with the
Department of Computer Science, Jamia
Millia Islamia (A Central University) New Delhi and
Directorate of Education, Govt. NCT of Delhi. He has a rich
Academics & Research experience in various areas of
Computer Science. His research interest includes: Software
Security, Requirements Engineering, Security Policies and
Standards, Formal Methods, Cloud Computing, Security
Metrics, Vulnerability Assessment etc. He has published
around 46 high quality research papers and articles in various
acclaimed International/National Journals (including IEEE,
ACM, CSI) and Proceedings of the reputed International/
National Conferences (including Springer). Out of these
publications, most of them have good citation records. He
has been nominated in the board of editors/reviewers of
various peer-reviewed and refereed Journals. In addition, he
has also served as a Program Committee Member of several
reputed conferences in India as well as abroad.
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