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An Enhanced Personal Profile Ontology for Software Requirements
Engineering Tasks Allocation
*Usip, P. U., Udo, E. N. and Umoeka, I. J.
Computer Science Department, University of Uyo, Uyo, Nigeria
{patienceusip@uniuyo.edu.ng; edwardudo@uniuyo.edu.ng; iniumoeka@uniuyo.edu.ng}
*Corresponding E-mail: patienceusip@uniuyo.edu.ng
Abstract
The availability of a web application for allocating software requirements engineering task to qualified personnel
requires personal profile ontology (PPO) which includes both static and dynamic features. Several personal profile
ontologies have been developed and deployed, but the personnel information represented are static leaving out very
important and dynamic properties of the personal data suitable for task handling in applications such as allocation of
task during the software requirement engineering processes. Personal profile is often modified for several purposes
calling for augmentation and annotation when needs arise. Resume is one resulting extract from personal profile and
often contain slightly different information based on needs. The urgent preparation of resume may introduce bias and
incorrect information for the sole aim of projecting the personnel as being qualified for the available job. This work
is aimed at providing an enhanced personal profile ontology for software requirements engineering task allocation that
captures both static and dynamic properties of personal data. The enhanced personal profile ontology (e-PPO) is a
constraint-based semantic data model developed using protégé. A web interface that deploys the e-PPO is also
developed using HTML and JavaScript and resulting smart resumes obtained from the populated instances in the
ontology will aid the decision and selection of the most qualified personnel for any queried software requirements
engineering task.
Keywords: Intelligent System, Personnel Selection, Semantic Web, Smart Resume.
1. Introduction
In our day-to-day transactions, we are faced with situations where we need to take decision and
make choice among several alternatives. Decision making process is about selecting the most
suitable alternative(s) according to certain criteria. This process is considered to be tough for
decision makers because of its uncertainty and subjectivity (Bai and Chen, 2008; Lin, 2010). This
process therefore requires a systematic and logical approach in order to make the correct choice.
Several works pointing to ontology-based decision support system capable of automatically
suggesting the best suited human resources for specific task have been done (Paredes-Valverde,
del Pilar Salas-Zárate, Colomo-Palacios, Gómez-Berbís and Valencia-García, 2018); but for quick
access to the needed resources for enhanced and effective decision, these decision support systems
should be Sematic Web driven.
Semantic Web came with its important objective to provide Web information with a well-defined
meaning that makes it understandable to both humans and computers (Paredes-Valverde, et al.,
2014). Ontologies are the fundamental technology for modeling the domain information.
Ontology, a formal and explicit specification of a shared conceptualization (Studer, Benjamin and
Fensel, 1998; Usip and Ntekop, 2016), provides reusable and sharable knowledge with a formal
and structured representation. Ontologies used for user profiling are mostly limited to taxonomies
of user interests. Other domain knowledge such as software requirement engineering and
development require the use of such technology for explicit specification.
Guidelines requiring the development of any software product are specified in the software
requirements (SR) phase of the Software Development Life Cycle (SDLC). The requirements for
a system are the descriptions of what the system should do which reflect the needs of customers
for a system that serves a certain purpose. The process of finding out, analyzing, documenting and
checking these services as well as their constraints is called requirements engineering (RE)
(Sommerville, 2011). Requirements are specified at the beginning of the development process and
these requirements specifications are used as guidelines for the software development (Couto et
al., 2014). IEEE Std 1233 (1998) defines requirement as a condition or a capability that must be
met or possessed by a system to satisfy a contract, standard, specification, or other formally
imposed document.
Software Requirements Engineering (SRE) provides the appropriate mechanism for understanding
what the customer wants, analyzing need, assessing feasibility, negotiating a reasonable solution,
specifying the solution unambiguously, validating the specification and managing the
requirements as they are transformed into an operational system (Sommerville, 2011). Software
requirement engineering is a well-defined process to identify stakeholders and their needs and also
documents such requirements for proper system implementation (Mustafa et al., 2018). SRE is a
sub-category of (RE) that deals with the elicitation, specification, and validation of requirements
for software (Bourque and Dupuis, 2004) and it is critical for successful software development.
SRE processes and activities are as outlined in Table 1.
Selecting appropriate personnel with the requisite qualification and skills for SRE tasks becomes
a major challenge considering the various components of the SR tasks and the diverse computing
skills and the numerous entries in the e-PPO. Decision making plays a vital role in real time
applications where there are many decision criteria (Sona et al., 2018). For modeling uncertainties
in industrial, natural and human systems, fuzzy sets and fuzzy logic are powerful mathematical
tools to adopt in order to facilitate decision-making as they use approximate reasoning and
linguistic terms (Tavana et al., 2013). However, an abridged information that is handy can go a
long way to help in decision making without the use of these mathematical tools with its numerous
and rigorous computations. Hence, the need for an abridged curriculum vitae that is Semantic Web
driven referred to as the smart resume in this work.
Table 1: Software Requirement Engineering Processes and Activities (Source: Sommerville, 2011; Pressman and Maxim, 2014)
S/N
PROCESS
SUB-PROCESS
DESCRIPTION/ACTIVITES
1
Feasibility
Study
Problem Analysis
• Assessing if the system is useful to the client
• Stating the problem, problem domain and environment
• Understanding the system behaviour and constraints in the
system
• Knowing the system inputs and outputs (from output of
existing system)
2
Elicitation and
Analysis
1. Requirement
Discovery
2. Requirement
Classification and
organization
3. Requirement
prioritization and
negotiation
• Meeting with clients and stakeholders – those that will
interact with the system and be affected by the system
• Users and customers ask questions about the system (scope,
what they need, evolution etc.)
• Finding out about the application domain, services to be
provided, required performance, hardware constraints etc.
From documents describing the organization and work)
• Discovering all requirements
• Organizing and describing the requirements
• Ranking requirements (by stakeholders, customers and users)
• Resolving priority conflict
• Identification and analysis of risk associated with each
requirement
3
Specification
Elaboration
• Address issues such as representation, specification
language, tools to use, etc.
• Produce Software Requirements Specification (SRS)
Documents – should include natural language description,
graphical models, scenarios
• Developing a refined technical model of software functions
and features together with their constraints using UML
diagrams, Use Cases, Data Flow Diagrams, Entity
Relationship Diagrams, analysis models etc.
4
Validation
Review and
Inspection
• Checking that the requirements define the system the
stakeholders want by reviewing the SRS document and
examines the specification
• Checking for errors (omission, inconsistency, incorrect fact,
ambiguity) in requirement specification and other factors
affecting quality
• Ensuring that work conforms to standards established for the
process, project and product.
• Carry out validation, consistency, completeness, realism
checks as well as verifiability.
• Adoption of some validation techniques such as requirement
review, prototyping and test case generation.
5
Requirement
Management
Planning
• Requirement identification
• Management of changes in requirement
• Tracing the relationship between each requirement and the
system design (Traceability policies)
• Knowing the tools for processing of requirement information
• Managing relevant information and knowledge.
The selection process is focused on personnel whose profiling has already been captured in the
enhanced Personal Profile Ontology (e-PPO). The e-PPO is a variation of the existing Personal
Profile Ontologies (PPO) which intends to capture the static and dynamic properties of the user.
2. Related Literature
Several approaches have been employed in resume parsing aimed at producing a smart resume.
Some of the approaches include the Two-Step Resume Information Extraction Algorithm
involving the text block identification and name entity recognition. (Chen, Zhang and Niu, 2018);
Statistical and rule-based (Jiang, Zhang, Xiao and Lin. (2009), entity linking paradigm (Deepak,
Teja and Santhanavijayan, 2020), named entity clustering algorithm (Sonar and Bankar, 2012),
Combination of neural networks and conditional random fields (Ayishathahira, Sreejith and
Raseek (2018) and NLP (Sanyal, Hazra, Adhikary and Ghosh, 2017; Sadiq, Ayub, Narsayya,
Ayyas and Tahir, 2016) approaches.
Ontologies have been deployed as efficient and intelligent knowledge management tools for
timetabling (Usip and Ntekop, 2016) and as a secure semantic smart healthcare (Tiwari, Jain,
Abraham and Shandilya, 2018; Mishra and Jain, 2019). The use of ontologies for user profile
creation can be traced back to (Maria, Akrivi, Costas, George and Constantin, 2007), with methods
and applications carefully outlined. Requirements and knowledge engineering processes adopt the
personal profile ontology in their reasoning (Sim, and Brouse, 2014); towards an ontology-based
persona-driven requirements and knowledge engineering.
Suárez-Figueroa, and Gómez-Pérez (2012) built ontology requirements specification, a reference
ontology, to provide a consensual knowledge model of the employment domain to be used by
public e-employment services. The application of ontologies has also been made in identifying
requirements patterns in use cases (Couto, Ribeiro and Campos, 2014). An ontology-based
approach was adopted in assigning human resources to software projects (Paredes-Valverde et al.,
2018), but their work took into consideration only the static personnel profile properties.
This work is an enhancement of personal profile ontology that captures the dynamic properties of
personal data in addition to the static ones in order to allocate task in software requirements
engineering domain.
3. Materials and Methods
Personnel selection process for SRE tasks requires explicit representation of the domain
knowledge as well as clearly documenting the tasks for appropriate and corresponding personnel
selection. This involves modeling the personal profile with both static and dynamic properties,
obtaining the criteria required for the selection of any SRE task and their relative importance
(weight).
3.1 The Enhanced Personal Profile Ontology (e-PPO)
Properties of the ontology such as name, gender, date of birth, etc. are static in nature and may not
change. Other static properties such as educational qualification and skills acquired can be updated
while others including area of specialization and profession are dynamic and can be changed. Also,
areas of specializations cannot be out of the scope of one’s profession, although one personnel can
have multiple profession and skills, more than one profession can share things in common such as
the same professional qualification may be required, and so on. Hence, Figure 1 shows the class
hierarchy of the e-PPO with both static and dynamic properties of personal profile while Figure 2
gives an ontograf that shows excerpts from the e-PPO.
Figure 1: Class hierarchies in Enhanced Personal Profile Ontology
Figure 2: Excerpts from Enhanced Personal Profile Ontology
From this ontology, the software requirements engineering documents and the most qualified
personnel can be formalized. Educational Qualification, Profession, Area of Specialization and
Skills Acquired are the most sensitive properties of the PPO. Formalizing the ontology and the
competencies of suitable personnel with varying properties for the SRE task is based on the relative
importance of the well labeled criteria and sub-criteria given in section 3.2.
3.2 Formalizing the SRE Task Allocation for the most qualified Personnel
Before the formal representation, several weights have been assigned based on experts’ view. The
criteria are weighted via linguistic expressions of relative importance. The weights of the criteria
for the personnel selection are determined by the decision maker using the following linguistic
variables: Absolutely Important (A.I), Strongly Important (S.I), Fairly Important (F.I), Weakly
Important (W.I) and Rarely Important (R.I). The criteria used in the personnel selection process
and the relative importance of each criteria and sub-criteria are shown in Table 2.
Table 2: Relative Importance of Criteria and the Sub-Criteria
Criteria
Criteria
Label
Relative
Importance
Sub-Criteria
Criteria
Label
Relative
Importance
EDUCATIONAL
QUALIFICATION
EDQ
A.I
Ph.D
Ph.D
F.I
M.Sc
M.Sc
S.I
PGD
PGD
R.I
B.Sc/HND
B.Sc/HND
A.I
ND/NCE
ND/NCE
W.I
PROFESSION
PRO
S.I
Computer Science
CS
A.I
Business Management
BM
F.I
Information Technology
IT
S.I
AREAOF
SPECIALIZATION
AOS
A.I
Software Engineering
SE
A.I
Project Management
PM
F.I
Management Information System
MIS
S.I
Business/Data Analytics
BDA
F.I
System Analysis
SA
A.I
InformationTechnology Management/
Entrepreneurship
ITME
F.I
Operations Management
OM
W.I
SKILL ACQUIRED
SKA
S.I
Knowledge Management
KM
A.I
Quality Assurance/Engineering
QAE
F.I
Data/Information Analysis
DIA
A.I
Technical Writing
TW
S.I
System Development/Testing
SDT
S.I
Software ProductivityTools Usage
SPT
S.I
Information Technology Risk
Management
IRM
F.I
From Table 1, it is obvious that any personnel must possess necessary educational qualifications
in the required profession before being considered for task allocation. The consideration must be
based on specific areas of specialization and applicants must have acquired sufficient skills in
related area.
Formal rules representing the combinations of the criteria A to D (where A represents EDQ; B
represents PRO; C represents AOS; D represents SKA) and sub-criteria of concern by the human
resource agent in charge of software requirements engineering task allocation given in Table 1 are
stated in rules AR1 – AR12; BR1 –BR7; CR1 – CR44; DR1 - DR35, where stands for
implication; for logical AND; for logical OR.
Some rules guiding the evaluation of applicant’s Educational Qualification (EDQ), Profession
(PRO), Area of Specialization (AOS) and Skills Acquired (SKA) using the possible combinations
of the sub criteria in Table 2 are written down thus:
Considering an instance where three personnel, P1, P2 and P3, have scaled through the different
stages of the recruitment process and the human resource officer is now faced with the decision of
whom to employ as the SRE analyst among the three personnel. Assuming the set of rules that
fired to shortlist the three personnel were:
P1 (AR1, BR1, CR2, DR8)
P2 (AR3, BR4, CR40, DR2)
P3 (AR4, BR2, CR4, DR10)
Deciding the best personnel out of the three will require a smart resume of the three personnel.
The model will then lookup for the components of the criteria and sub-criteria from the rules for
each personnel as depicted by the architecture in Figure 3.
3.3 The Ontology-Based Personnel Selection System Architecture
Figure 3 shows the architecture of the ontology-based intelligent system, where the formalized
rules are built into the e-PPO and used during the selection process in order to eliminate bias due
to conflicting interest and also select intelligently the most competent personnel for the SRE tasks
using the resulting smart resume.
Figure 3: The Ontology based Personnel Selection System Architecture
From the architecture, the applicant completes the application process as input to the e-PPO
model,and the enhanced personal profile ontology is used to generate competent personnel. The
selection officer views the competent personnel in the light of the organization’s selection criteria
to generate the most qualified personnel.
4. Results and Discussion
The resulting sample smart resume from the illustration with personnel P1, P2 and P3 in section 3.2
is as given in Table 3, where the numbers 5, 4, 3, 2, 1 are equivalent to the linguistic variables:
Absolutely Important (A.I), Strongly Important (S.I), Fairly Important (F.I), Weakly Important
(W.I) and Rarely Important (R.I) respectively.
Table 3: A Sample Smart Resume
PERSONNEL_ID
RULES
EDQ
PRO
AOS
SKA
Computed
Relative
Importance
Total
Points
P1
AR1, BR1,
CR2, DR8
PhD, MSc,
BSc
CS
SA
IRM
3+4+5, 5,
5,3
25
P2
AR3, BR4,
CR40, DR2
PhD, MSc,
HND, ND
CS,
BM
SA, MIS,
PM, OM
DIA
3+4+5+2,
5+3,
5+4+3+2,
5
41
P3
AR4, BR2,
CR4, DR10
PhD, MSc,
HND, ND,
NCE
BM
SE, SA,
MIS
KM,
TW
3+4+5+2+
2, 3,
5+5+4,
5+4
42
From the sample smart resume, P1 has 12 points for EDQ; 5 points for PRO; 5 points for AOS and
3 points for SKA, giving him a total of 25 points. P2 has 14 points for EDQ; 8 points for PRO; 14
points for AOS and 5 points for SKA, giving him a total of 41 points. P3 has 16 points for EDQ; 3
points for PRO; 14 points for AOS and 9 points for SKA, giving him a total of 42 points.
Personnel P3 with total points of 42 is the most competence for the software requirements
engineering process task followed by P2 with total points of 41. Personnel P1 is the least qualified
with total points of 25. Therefore, P3 is qualified for the SRE analyst position.
5. Conclusion
Decision making process is about selecting the most suitable alternative(s) according to certain
criteria. The enhanced personal profile ontology was created. Information represented in the
ontology include static and dynamic properties of the personal profile suitable for task handling in
applications such as promotion appraisal, and allocation of task during the software requirement
engineering process. Selecting the most suitable alternative(s) according to certain criteria is
sometimes considered to be a tough task for decision makers because of its complexity and
subjectivity. The suitability of personnel’s properties in this ontology for the software requirements
engineering task allocation is returned based on the formal rules that fires. With these formal rules
built into the e-PPO, the static and dynamic features of personal profile can be analysed given the
smart resume.
References
Ayishathahira, C. H., Sreejith, C., & Raseek, C. (2018, July). Combination of neural networks and conditional random
fields for efficient resume parsing. In 2018 International CET Conference on Control, Communication, and
Computing (IC4) (pp. 388-393). IEEE.
Bai, S. M and Chen, S.M (2008). Automatically Constructing Grade Membership Functions of Fuzzy Rules for
Students’ Evaluation. Expert Systems with Applications 35(3): 1408 - 1414. doi:10.1016/j.eswa.2007.08.022
Bourque, P. and R. Dupuis, R. (2004). Guide to the Software Engineering Body of Knowledge, IEEE Computer
Society, Washington DC.
Burney, S. A., Ali, S. M and Burney, S. (2017). A survey of soft computing applications for decision making in
supply chain management. In Proceeding of IEEE 3rd International Conference on Engineering Technologies and
Social Sciences (ICETSS), pp 1–6.
Burney, S. and Ali, S. (2019). Fuzzy Multi-Criteria Based Decision Support System for Supplier Selection in Textile
Industry, 19(1), 239 – 244.
Calegari, S., and Ciucci, D. (2006). Integrating fuzzy logic in ontologies. In ICEIS (2) (pp. 66-73).
Couto, R., Ribeiro, A. and Campos, J. (2014). Application of Ontologies in Identifying Requirements Patterns in Use
Cases. Formal Engineering Approaches to Software Components and Architectures, 147, 62 – 76, doi:
10.4204/EPTCS.147.5
Deepak, G., Teja, V., & Santhanavijayan, A. (2020). A novel firefly driven scheme for resume parsing and matching
based on entity linking paradigm. Journal of Discrete Mathematical Sciences and Cryptography, 23(1), 157-165.
IEEE Std 1233 (1998). IEEE Guide for Developing System Requirements Specifications, 1998 Ed., pp. 1–36.
Iftikhar, A., Musa, S., Alam, M., Su’ud, M. M., & Ali, S. M. (2018). Application of Soft Computing Techniques in
Global Software Development: state-of-the-art Review. International Journal of Engineering & Technology, 7(4.15),
304-310.
Jiang, Z., Zhang, C., Xiao, B., & Lin, Z. (2009, January). Research and implementation of intelligent chinese resume
parsing. In 2009 WRI international conference on communications and mobile computing (Vol. 3, pp. 588-593). IEEE.
Jie Chen,1 Chunxia Zhang,2 and Zhendong Niu (2018). A Two-Step Resume Information Extraction Algorithm.
Mathematical Problems in Engineering. Volume 2018, Article ID 5761287, 8 pages, Hindawi,
https://doi.org/10.1155/2018/5761287
Khan, M., Manzoor, A., Rohail, K., Ali, S. M., Iftikhar, A., &Alam, M. (2018, May). Soft computing applications in
education management—A review. In 2018 IEEE International Conference on Innovative Research and Development
(ICIRD) (pp. 1-4). IEEE.
Maria, G., Akrivi, K., Costas, V., George, L., & Constantin, H. (2007). Creating an ontology for the user profile:
Method and applications. In Proceedings AI* AI Workshop RCIS.
Mishra, S., & Jain, S. (2019). Towards a Semantic Knowledge Treasure for Military Intelligence. In Emerging
Technologies in Data Mining and Information Security (pp. 835-845). Springer, Singapore.
Mustafa, A. Shah, M. and Younas, M. (2018). Integration of Heterogeneous Requirements using Ontologies.
International Journal of Computer Science and Applications, 9(5), 213 – 218
Paredes-Valverde, M. A., del Pilar Salas-Zárate, M., Colomo-Palacios, R., Gómez-Berbís, J. M., & Valencia-García,
R. (2018). An ontology-based approach with which to assign human resources to software projects. Science of
Computer Programming, 156, 90-103.
Pressman, R. and Maxim, B. (2014). Software Engineering: A Practitioner’s Approach (Eight Edition). McGraw-Hill
Education, New York.
Sadiq, S. Z. A. M., Ayub, J. A., Narsayya, G. R., Ayyas, M. A., & Tahir, K. T. M. (2016). Intelligent hiring with
resume parser and ranking using natural language processing and machine learning. International Journal of
Innovative Research in Computer and Communication Engineering, 4(4), 8.
Sanyal, S., Hazra, S., Adhikary, S., & Ghosh, N. (2017). Resume parser with natural language
processing. International Journal of Engineering Science, 4484.
Sim, W. W., & Brouse, P. (2014). Towards an ontology-based persona-driven requirements and knowledge
engineering. Procedia Computer Science, 36, 314-321.
Sommerville, I (2011): Software Engineering, Ninth Edition. Pearson Education Inc, Boston, Massachusetts, USA
Sonar, S., & Bankar, B. (2012). Resume parsing with named entity clustering algorithm. paper, SVPM College of
Engineering Baramati, Maharashtra, India.
Studer, R, Benjamin, V. R. and Fensel, D. (1998). Knowledge engineering Principles and methods. Data Knowledge
Engineering, Vol. 25, No. 1 pp. 161-197.
Suárez-Figueroa, M. C., & Gómez-Pérez, A. (2012). Ontology requirements specification. In Ontology Engineering
in a Networked World (pp. 93-106). Springer, Berlin, Heidelberg.
Tavana, M., Azizi, F., and Behzadian, M. (2013). A Fuzzy Inference System with Application to Player Selection and
Team Formation in Multi-player Sports, Sport Management Review, 16(1), 97-110.
Tiwari, S. M., Jain, S., Abraham, A., & Shandilya, S. (2018). Secure Semantic Smart HealthCare (S3HC). Journal of
Web Engineering, 17(8), 617-646.
Usip, P. U. and Ntekop, M. M. (2016). The Use of Ontologies as Efficient and Intelligent Knowledge Management
Tool. In proceedings of Future Technologies Conference 2016 – FTC 2016, 6-7 December 2016, San Francisco,
United States.
