International Journal of Scientific & Engineering Research, Volume 4, Issue 8, August-2013 889

ISSN 2229-5518

Requirements Analysis Based on Ontology

Techniques

Roshan Bande, Prof. Kapil Hande

Abstract— We have designed a method by using which one can analyse the requirement document of the software. It is mapping between software requirement specification and the knowledge base. To represent the knowledge base we have chosen ontology techniques. Our designed system consists of Natural language processing part in which we can separate the parts of speech by tagging them. Natural language generator develops sentences for putting output in the form of natural language. More precisely we developed a system in which input is the SRS document and the output will be a report in which the detailed information will be presented about the SRS document. We have taken care of incompleteness inconsistency, accuracy on these criteria a final verdict is presented weather the SRS document is acceptable or not. Experimental results shows RABOT is indeed a good analysing technique for SRS document analysis.

Index Terms— ontology, analysis, knowledge base, artificial intelligence, Expert system, Automation, SRS document.

——————————  ——————————

1 INTRODUCTION

ne of the goals of requirements analysis is to develop a requirements specification document of high quality. There are several methods to achieve this goal and their supporting tools are going to be used in practice, e. g., goal oriented requirements analysis methods, scenario analysis, use case modelling techniques and so on. One of the most crucial problems to automate requirements analysis is that require- ments documents are usually written in natural language, e. g. English or Japanese. Although techniques for natural language processing (NLP) are being advanced nowadays, it is hard to handle such requirements documents sufficiently by comput- er. However, semantic processing in requirements is indispen- sable for producing requirements specifications of high quali- ty. To overcome the problem, there are several approaches, but each of them has its inherent problems. In some studies, a semi-formal notation for representing requirements, e. g. re- stricted natural languages was introduced, but it was difficult for human engineers to write syntactically and semantically correct requirements sufficiently by using this notation. Rigor- ous formal notations with axioms and inference system seem to be suitable, but its usage is very limited to practitioners be- cause of their difficulty and complexity in the practitioners'
learning and training.
We use an ontology system to develop a software require-
ments document of high quality. Ontology technologies are
frequently applied to many application domains nowadays,
because concepts, relationships and their categorizations in a
real world can be represented in ontology. Ontology can be

————————————————

• Roshan Bande is currently pursuing masters degree program in computer science and engineering in RTM NagpurUniversity , India,.

E-mail: roshan.bande@rediffmail.com

• Prof. Kapil Hande, Assistant Professor,Dept. of Computer Science & Engi- neering E-mail: kapilhande@gmail.com

used as resources of domain knowledge, especially in a specif- ic application domain. By using such ontology, several kinds of semantic processing can be achieved in requirements analy- sis without rigorous NLP techniques.
In this paper, we design a requirements analysis method by using an ontology technique, where we establish a mapping between a requirements specification and ontological ele- ments. This technique allows us to have the possibility of au- tomating semantic analysis with lightweight processing, not heavyweight NLP techniques. By mapping requirements de- scriptions in a requirements document onto ontological ele- ments, which represents fragments of meaning in a problem domain, each description can be semantically interpreted. By applying inference rules to the ontological elements, we can achieve semantic processing about the requirements docu- ment.

2 RELATED WORK

“Requirements Analysis and Prototyping using Scenarios and State charts approach” uses precise action semantics, sup- ports changing requirements and enables seamless generation of a fully functional prototype for end user requirements vali- dation. The method is currently being implemented in the STAMP tool (State Modelling and Prototyping).
“Real-time fault diagnosis using knowledge-based expert system” demonstrates that diagnosis methodology is com- prised of three steps (Fig. 1) to detect fault. Very first step is acquiring the real-time process information, from critical equipments, such as boilers, compressors, separators or reac- tors. Temperature, pressure, level, and flow rate are the most important process variables to be monitored and have the ca- pability of representing the state of operation in a variety of equipments. The disorder in these objects can affect the stabil- ity and safety of the whole process system. The second step is making inferences (diagnosis) judge on acquired process in- formation. The last step is acting as per inference instruction, such as informing operators, raising alarms, shutting down equipment, activating higher layer protections and trying to

International Journal of Scientific & Engineering Research, Volume 4, Issue 8, August-2013 890

ISSN 2229-5518

bring the system back to normal condition.
Fig. 1. Three steps of methodology
“ONTOLOGY FOR MOBILE PHONE OPERATING SYS- TEMS” is the ongoing study deals with an important part of a line of research that constitutes a challenging burden. It is an initial investigation into the development of a Holistic Frame- work for Cellular Communication (HFCC). The main purpose is to establish mechanisms by which existing wireless cellular communication components and models can work holistically together. It demonstrates that establishing a mathematical framework that allows existing cellular communication tech- nologies (and tools supporting those technologies) to seam- lessly interact is technically feasible. The longer-term future goals are to actually improve the interoperability, the efficien- cy of mobile communication, calls quality, and reliability by applying the framework to specific development efforts.
"An Automatic Quality Evaluation for Natural Language Requirements" states there is need of software quality analy- sis. This system uses natural language processing technique to check the construction of sentences and structure of SRS doc- ument.

3 EXISTING SYSTEM

“Advanced and Innovative Models And Tools for the de- velopment of Semantic-based systems for Handling, Acquir- ing, and Processing knowledge Embedded in multidimen- sional digital objects” by Information society technology pur- sued innovations towards digital representations of shapes capable of modelling not only the visual appearance of objects but also their meaning or functionality in a given knowledge domain. In this setting, shape knowledge has been concerned with the geometry (the spatial extent of the object), the struc-

4 METHODOLOGY

The designed system is shown in the flowchart given. In which all the modules are separated on each step. Reading line of SRS document is line by line and each line will be processed to extract keywords. Each keyword will be then fired on on- tology the working of it is shown in fig. 3 and fig. 4.

The result generated on query firing will be presented in natural language form using NLG techniques.
Fig2 Flowchart of the designed system
Designing and development of ontology will be the major task as the ontology will work as the knowledge base in the designed system. The inference engine will be either prolog or any other existing system which will be used as inference en- gine.
ture (object features and part-whole decomposition), attributes (colours, textures), semantics (meaning, purpose), and has had interaction with time (morphing, animation). The harmoniza-

Require- quire-

Inference

Engine

Ontology

tion of shape modelling approaches in Computer Graphics and Computer Vision has been pursued via the definition of shared vocabularies and ontologies, not only for the above- mentioned specific domains, but also on a higher level as the basis for the project's eScience platform, the Digital Shape Workbench. As the project's main technological innovation, this workbench served the role of an operational, large-scale, distributed and web-based software system serving as com- mon infrastructure. The scientific innovation sought by this project is focused on modelling the semantics of digital shapes at each stage of their lifecycle.

Fig3 . working of designed sys- tem

Fig 2 shows the block structure of the designed system . Requirements are input for inference engine inference engine will then perform the guided operation to analyse the re- quirements in the ontology and show the output as the result of this operation. More detailed working is shown in Fig 3.

International Journal of Scientific & Engineering Research, Volume 4, Issue 8, August-2013 891

ISSN 2229-5518

of suggestions so that user can think whether they have to consider for writing the document. These keywords are checked against the ontology of the organization, all the pos- sible linked aspects will be covered which will lead to approx- imately perfect requirement document.
The expected output of the system is the classes that are re- lated to the keyword present in the requirement document. Such as
1. Name of organization has relation with tagline, phone number, email address.
2. Copyright information has relation with license docu- ment.

Fig 4. Mapping from requirement to ontology

Fig 3 illustrates mappings from requirements items (state- ments) in a requirements document to elements in ontology. The requirements document may be described in advance, or it may be described incrementally through the interaction be- tween a requirements analyst and stakeholders. The require- ments document is analysed by using this kind of mappings. For example, OBSRA may suspect a requirements document is incomplete when not all elements in an appropriate ontology are related to items in the document. The mapping between the statements and ontology has to be done by using a frame of natural language. OBSRA checks whether a requirements document is consistent or complete by using an ontology sys- tem each requirements item (statement) is mapped onto a set of elements (concepts and relationships) in the thesaurus of the ontology system. To detect inconsistency of a requirements document, designed system try to find mutually contradicting elements where requirements items are mapped. For example, designed system decide the document is inconsistent if there is a relationship "contradict" between two concepts where the document is mapped. To detect incompleteness of a require- ments document, designed system follow specific relation- ships from concepts where the document is already mapped. For example, designed system follow "require" relationship and find a concept that does not appear in the current docu- ment. Then, designed system add new requirements items (statements) corresponding to the concept.
For the sake of example, we assume a requirement docu-
ment
1. Home page should have Logo
2. Home page should have name of organization
3. Home page should contain information about organiza-
tion
4. Homepage should have copyright information
5. Homepage should have Image slider showing the work of
organization
6. Home page should have quick form to get the information
of user.
Fig 3 shows the input to the system will be requirement documents similar as stated above.
Requirement document is analyzed by our system to find if the keyword is present in the ontology or not if it is present then the linked classes will be put in front of the user in terms
Ontology development requires thorough study of domain in which it is being developed also it requires keeping account of all relationships. The importance of protégé is demonstrat- ed clearly while developing the ontology. There are several features that distinguish Protégé from other knowledge base editing tools. To the best of our knowledge, no other tool ex- cept Protégé has all of the following features: Intuitive and easy-to-use graphical user interface. Scalability: Protégé's da- tabase back-end loads frames only on demand and uses cach- ing to free up memory when needed. There is virtually no de- terioration in performance as you go from several hundred frames to several thousand frames. Extensible plug-in archi- tecture: We can easily extend Protégé with plug-ins tailored for our domain and task. Some ideas for plug-ins are: Small user-interface components that are particularly well suited to displaying and acquiring values in our domain. Such compo- nents could be used on Protégé forms. Custom back-end plug- ins that use our own storage mechanisms. New applications intricately linked with a knowledge base as a Protégé tab.
Prolog rules are used for the knowledge representation, and the Prolog inference engine is used to derive conclusions. Other portions of the system, such as the user interface, must be coded using Prolog as a programming language.
One tentative proposal to achieve the structure in fig. 2 might be that we develop an Ontology using Protege and we load it into Prolog, one of the most obvious consequences of this will be the ontology is accessible by prolog now proper programming will help us to achieve what we have designed.

5 EXPERIMENT AND RESULTS

An experiment designed in which ontology named “col- lege.owl” is first build which will work as knowledge base. A SRS document has been developed with a standard template of the SRS document.
SRS document is given as input to the designed system and analysed over the knowledge i.e. ontology. It was expected that the SRS document will be analysed line by line and each line will be process i.e. NLP by which keywords will be ex- tracted and each keyword will be fired as query on ontology the result will then be sent to generate natural language sen- tences i.e. NLG The result found is as follows:

International Journal of Scientific & Engineering Research, Volume 4, Issue 8, August-2013 892

ISSN 2229-5518

Fig.5 Relation of considered entities with entities not cov- ered.
Fig.6 Number of entities and missing entities its accuracy and approval.
As you can see the first entity in fig 5 is Library which is generated from the statement of the SRS document “The li- brary is the storage of books” in which a keyword library is found and related entities of the Library in the ontology is shown in the Related entities section

6 CONCLUSION

In this paper, we design a requirements analysis method by using ontology. Even though the method does not support rigorous natural language processing techniques (NLP), the method enables us to detect incompleteness and inconsistency about a requirements document, to measure the quality of the document, and to predict requirements changes in the future versions of the document. After defining the process to use our ontology approach, we will design and implement its supporting tools. There are many studies using NLP for re- quirements engineering. For example, inconsistencies in natu- ral language requirements are discovered, conceptual models are semi-automatically generated by linguistic analysis , or formal method and lightweight natural language processing are used together . However, it seems to be unclear how to handle domain knowledge and quality of requirements doc- ument itself in such studies. Studies to handle ambiguity in use case descriptions written in natural language exist but they also unclearly handled domain knowledge. How to de- velop ontology is to be studied. However, most methods for building ontology are ambiguous, thus the quality and effi- ciency of building ontology depend on the skills of each engi- neer . Therefore, we have to explore systematic procedure to build ontology. Normally, we focus on the frequency of the occurrences of words or phrases in the documents when we build ontology. In contrast to source codes, there are no uni- fied and formal languages in requirements documents thus it is hard to analyse them in requirements analysis. In our study, ontology plays a role to relate different versions of documents and their change histories with each other, thus we can predict changes in requirements documents. In our study, quality characteristics are also represented as concepts in ontology.
However, such characteristics are represented in a goal model and such goal model and ontology are combined in a study. We also have our own goal oriented requirements model, thus we try to explore the possibility to combine a goal model and ontology. With respect to extending a model for semantic pro- cessing, we have to take implementation issues into account. To add knowledge about implementation into ontology, tasks in design and implementation phases could be supported by the ontology.

REFERENCES

[1] K. K. Breitman and J. C. S. do Prado Leite. Ontology as a Re- quirements Engineering Product. In 11th IEEE International Re- quirements Engineering Conference (RE'03), ages 309-319, Sep.

2003.

[2] D. Zowghi, V. Gervasi, and A. McRae. Using Default Reasoning to Discover Inconsistencies in Natural Language Requirements. In Eighth Asia-Pacific Software Engineering Conference (APSEC'01), pages 113-120, Dec. 2001.

[3] N. Guarino and C. Welty. Evaluating Ontological Decisions with Ontoclean. Communications of the ACM, 45(2):61-65, Feb

2002.

[4] M. Khedr and A. Karmouch. Negotiation Context Information in Context-Aware Systems. IEEE Intelligent Systems, 19(6):21-

29, Nov/Dez 2004.

[5] O. Khriyenko and V. Terziyan. Context Description Framework for the Semantic Web. In Context Representation and Reasoning Workshop, Jul 2005.

[6] Jason I. Hong and James A. Landay, "An Infrastructure Ap- proach to Context-Aware Computing", Human-Computer In- teraction, Vol. 16, 2001.

[7] Harry Chen and Tim Finin, "An Ontology for a Context Aware Pervasive Computing Environment", IJCAI workshop on ontol- ogies and distributed systems, Acapulco MX, August 2003.

[8] Anand Ranganathan and Roy H. Campbell, "A Middleware for Context-Aware Agents in Ubiquitous Computing Environ- ments", In Proceedings of ACM/IFIP/USENIX International Middleware Conference, Rio de Janeiro, Brazil, June 2003.

[9] M. Smith, C. Welty, and D. McGuinness, Web Ontology Lanu- gauge (OWL) Giude, August 2003.

[10] Andy Harter, Andy Hopper, Pete Steggles, Andy Ward, Paul Webster, "The Anatomy of a Context-Aware Application", Wire- less Networks 8(2-3): 187-197(2002).

[11] H. Wu, M. Siegel, and S. Ablay, "Sensor Fusion for Context Un- derstanding", Proceedings of IEEE Instrumentation and Meas- urement Technology Conference, Anchorage, USA, May 2002.

[12] Henricksen K, Indulska J, Rakotonirainy A., "Modeling Context Information in Pervasive Computing Systems", In Proceedings Pervasive Computing, Zurich, August 2002.

[13] Karen Henricksen, Jadwiga Indulska, and Andry Rakotonirainy, "Generating Context Management Infrastructure from High- level Context Models", In Proceedings of the 4th International Conference on Mobile Data Management, Melbourne, January

2003.

[14] Held, A., Buchholz, S., Schill, A., "Modeling of Context Infor- mation for Pervasive Computing Applications", In Proceedings of the 6th World Multiconference on Systemics, Cybernetics and

International Journal of Scientific & Engineering Research, Volume 4, Issue 8, August-2013 893

ISSN 2229-5518

Informatics (SCI), Orlando, FL, July 2002.

[15] Ian Horrocks, "DAML+OIL: a Reason-able Web Ontology Lan- guage", In Proceedings of the 8th International Conference on Extending Database Technology (EDBT), Prague, March 2002.

[16] Dan Brickley, R. V. Guha, RDF Vocabulary Description Lan- guage 1.0: RDF Schema, World Wide Web Consortium, January

2003.

[17] Jena 2 - A Semantic Web Framework, http://www.hpl.hp.com/semweb/jena2.htm

[18] Tao Gu, H. C. Qian, J. K. Yao, H. K. Pung, "An Architecture for

Flexible Service Discovery in OCTOPUS", In Proceedings of the

12th International Conference on Computer Communications and Networks (ICCCN), Dallas, Texas, October 2003.

[19] Henricksen K, Indulska J, Rakotonirainy, "Infrastructure for

Pervasive computing: Challenges", Workshop on Pervasive

Computing INFORMATIK 01, Viena, September 2001.

[20] F. Fabbrini, M. Fusani, S. Gnesi, G. Lami, “An Automatic Quali- ty Evaluation for Natural Language Requirements”, Istituto di Elaborazione dell’Informazione del C.N.R. – Pisa, Italy

[21] Dey, A. and Abowd, G., "Towards a Better Understanding of Context and Context-Awareness", Workshop on the what, who, where, when and how of context- awareness at CHI 2000, April

2000.

[22] Dey, A. K., Salber, D. Abowd, G. D., "A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context- Aware Applications", Human- Computer Interaction (HCI) Journal, Vol. 16(2-4), pp. 97-166, 2001.

[23] T. Kindberg and J. Barton, "A Web-based Nomadic Computing

System", Computer Networks, 35(4):443-456, 2001.

[24] Information Society Technology –“Advanced and Innovative Models And Tools for the development of Semantic-based sys- tems for Handling, Acquiring, and Processing knowledge Em- bedded in multidimensional digital objects” http://cordis.europa.eu/ist/kct/aimatshape_synopsis.htm .

[25] C Nan, F Khan, MT Iqbal “Real-time fault diagnosis using

knowledge-based expert system” - process safety and environ- mental protection, 2008 – Elsevier

[26] E. Kamsties, B. Peach Taming Ambiguity in Natural Language

Requirements. ICSSEA 2000-5.

[27] Fantechi, M. Fusani, S. Gnesi, G. Ristori. Expressing properties of software requirements through syntactical rules. Technical Report. IEI-CNR, 1997.

[28] Fantechi, S. Gnesi, G. Ristori, M. Carenini, M. Vanocchi, P. Mo- reschini, "Assisting requirement formalization by means of nat- ural language translation", Formal Methods in System Design, vol 4, n. 3, pp. 243-263, Kluwer Academic Publishers, 1994

[29] J. Krogstie, O. I. Lindland, G. Sindre. Towards a deeper under- standing of quality in requirements engineering. In 7th Interna- tional CAiSE Conference, vol. 932 of Lecture Notes in Computer Science, pages 82-95, 1995.

[30] G. Lami. Towards an Automatic Quality Evaluation of Natural

Language Software Specifications. Technical Report. B4-25-11-

99. IEI-CNR, 1999.

[31] Ben Morris, the Symbian OS Architecture Source book Design and Evolution of a Mobile Phone OS, John Wiley & Sons, Ltd

2007.

[32] Stefan Brahler, "Analysis of the Android Architecture", Karls- ruhe institute for technology, October 2010.

[33] Niles, I & Pease A. (2001) "Towards A Standard Upper Ontolo- gy." In Proceedings of FOIS 2001, October 17-19, Ogunquit, Maine, USA.

[34] Wang, P., Xu, B. LILY: “The Results for the Ontology Alignment Contest” OAEI 2007. In Proceedings of ISWC 2007 Ontology Matching Workshop. Busan, Korea.

[35] O. Khriyenko and V. Terziyan. Context Description Framework for the Semantic Web. In Context Representation and Reasoning Workshop, Jul 2005.

[36] O. Lassila and D. Khushraj. Contextualizing Applications via Semantic Middleware. In 2nd Ann. Int'l Conf. on Mobile and Ubiquitous Systems: Networking and Services, Jul 2005.

[37] D. L. McGuinness and F. van Harmelen. OWL Web Ontology

Language Overview. W3C Recommendation, Feb 2004.

[38] Jawad Makki, Anne-Marie Alquier, Violaine Prince, `An NLP- based ontology population for a risk management generic struc- ture', 5th International Conference on Soft Computing as trans- disciplinary Science and Technology 2008, pages 250-355.

[39] Christian Cuske, Tilo Dickopp, Stefan Seedorf `An ontology based platform for knowledge based simulation modeling in fi- nancial risk management', European simulation and Modeling Conference 2005

[40] Dillon, T. S., Chang, E., Wongthongtham. P, `Ontology-Based Software Engineering - Software Engineering 2.0',19th Australi- an Conference on Software Engineering, 2008, 26-28 March

2008, Page(s):13-23