An Analysis of Ambiguity Detection Techniques for Software Requirements Specification (SRS)

  • Authors

    • Khin Hayman Oo
    • Azlin Nordin
    • Amelia Ritahani Ismail
    • Suriani Sulaiman
    2018-05-22
    https://doi.org/10.14419/ijet.v7i2.29.13808
  • Ambiguity, SRS, Techniques
  • Ambiguity is the major problem in Software Requirements Specification (SRS) documents because most of the SRS documents are written in natural language and natural language is generally ambiguous. There are various types of techniques that have been used to detect ambiguity in SRS documents. Based on an analysis of the existing work, the ambiguity detection techniques can be categorized into three approaches: (1) manual approach, (2) semi-automatic approach using natural language processing, (3) semi-automatic approach using machine learning. Among them, one of the semi-automatic approaches that uses the Naïve Bayes (NB) text classification technique obtained high accuracy and performed effectively in detecting ambiguities in SRS.

     

     

  • References

    1. [1] Carlson N, Laplante P. The NASA automated requirements measurement tool: a reconstruction. Innovations Syst Softw Eng. 2014;10:77-91.

      [2] Hussain I, Ormandjieva O, Kosseim L. Automatic quality assessment of SRS text by means of a decision-tree-based text classifier. Proceedings - International Conference on Quality Software. 2007(Qsic):209-18.

      [3] De Bruijn F, Dekkers HL. Ambiguity in natural language software requirements: A case study. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2010;6182 LNCS:233-47.

      [4] Bussel DV. Detecting ambiguity in requirements specifications. 2009(09).

      [5] Kiyavitskaya N, Zeni N, Mich L, Berry DM. Requirements for tools for ambiguity identification and measurement in natural language requirements specifications. Requirements Engineering. 2008;13(3):207-39.

      [6] Yang H, Willis A, Roeck AD, Nuseibeh B, De Roeck A. Automatic detection of nocuous coordination ambiguities in natural language requirements. Proceedings of the IEEE/ACM international conference on Automated software engineering SE - ASE '10. 2010:53-62.

      [7] Kamsties E, Berry DM, Paech B, Kaiserslautern D. Detecting Ambiguities in Requirements Documents Using Inspections. 2001:1-13.

      [8] Denger C, Berry DM, Kamsties E. Higher quality requirements specifications through natural language patterns. … : Science, Technology and …. 2003:1-11.

      [9] Havasi C, Speer R, Alonso JB. ConceptNet 3: a Flexible, Multilingual Semantic Network for Common Sense Knowledge. In Proceedings of Recent Advances in Natural Languges Processing 2007. 2007:1-7.

      [10] Hill E, Fry ZP, Boyd H, Sridhara G, Novikova Y, Pollock L, et al. AMAP: Automatically mining abbreviation expansions in programs to enhance software maintenance tools. Proceedings - International Conference on Software Engineering. 2008:79-88.

      [11] Körner SJ, Brumm T. RESI - A natural language specification improver. ICSC 2009 - 2009 IEEE International Conference on Semantic Computing. 2009:1-8.

      [12] Körner SJ, Brumm T. Improving natural language specifications with ontologies. Proceedings of the 21st International Conference on Software Engineering and Knowledge Engineering, SEKE 2009. 2009:552-7.

      [13] Wang X-Z, Dong L-C, Yan J-H. Maximum Ambiguity-Based Sample Selection in Fuzzy Decision Tree Induction. IEEE Transactions on Knowledge and Data Engineering. 2012;24(8):1491-505.

      [14] Misra J, Das S. Entity Disambiguation in Natural Language Text Requirements. 2013 20th Asia-Pacific Software Engineering Conference (APSEC). 2013:239-46.

      [15] Wijewickrema CM. Impact of an ontology for automatic text classification. Annals of Library and Information Studies. 2014;61(4):263-72.

      [16] Nakagawa T, Matsumoto Y. Detecting errors in corpora using support vector machines. Proceedings of the 19th international conference on Computational linguistics-Volume 1. 2002:709-15.

      [17] Polpinij J. An ontology-based text processing approach for simplifying ambiguity of requirement specifications. 2009 IEEE Asia-Pacific Services Computing Conference, APSCC 2009. 2009:219-26.

      [18] Polpinij J, Ghose A. An automatic elaborate requirement specification by using hierarchical text classification. Proceedings - International Conference on Computer Science and Software Engineering, CSSE 2008. 2008;1:706-9.

      [19] Seijas L, Segura E. Detection of ambiguous patterns using SVMs: Application to handwritten numeral recognition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2009;5702 LNCS:840-7.

      [20] Clark A, Giorgolo G, Lappin S. Statistical Representation of Grammaticality Judgements: the Limits of N-Gram Models. Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics. 2013:28-36.

      [21] Sharma R, Bhatia J, Biswas KK. Machine learning for constituency test of coordinating conjunctions in requirements specifications. Proceedings of the 3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering - RAISE 2014. 2014:25-31.

      [22] Allahyari-Abhari A, Soeken M, Drechsler R. Requirement Phrasing Assistance Using Automatic Quality Assessment. Proceedings - 2015 IEEE 18th International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2015. 2015:183-8.

      [23] Gause DC, Gause DC, Weinberg GM, Weinberg GM. Exploring requirement. Quality before design. 3. 1989.

      [24] Popescu D, Rugaber S, Medvidovic N, Berry DM. Reducing ambiguities in requirements specifications via automatically created object-oriented models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2008;5320 LNCS:103-24.

      [25] Anda B, Sjøberg DIK. Towards an Inspection Technique for Use Cases Models. Proceedings of the 14th international conference on Software engineering and knowledge engineering - SEKE '02. 2002(1325):127-34.

      [26] Osborne M, MacNish CK. Processing natural language software requirement specifications. Proceedings of the Second International Conference on Requirements Engineering. 1996:229-36.

      [27] Romano JJ, Palmer JD. TBRIM: decision support for validation/verification of requirements. Systems, Man, and Cybernetics, 1998 1998 IEEE International Conference on. 1998;3:2489-94 vol.3.

      [28] Subha R, Palaniswami S. Ontology extraction and semantic ranking of unambiguous requirements. Life Science Journal. 2013;10(2):131-8.

      [29] Mishra B, Shukla KK. Impact of attribute selection on defect proneness prediction in OO software. 2011 2nd International Conference on Computer and Communication Technology, ICCCT-2011. 2011:367-72.

      [30] Maroulis G. Comparison between Maximum Entropy and Naïve Bayes classifiers : Case study ; Appliance of Machine Learning Algorithms to an Odesk ’ s Corporation Dataset Georgios Maroulis Submitted in partial fulfilment of the requirements of Edinburgh Napier University. 2014(January).

      [31] Brown PF, DeSouza PV, Mercer RL, Della Pietra VJ, Lai JC. Class-Based n-gram Models of Natural Language. Computational Linquistics. 1992;18(1950):467-79.

      [32] Tyler Baldwin YLBAIRS. Automatic Term Ambiguity Detection. Acl. 2013;2:804-9.

      [33] Singh S. International Journal of Advanced Research in Computer Science and Software Engineering Ambiguity in Requirement Engineering Documents : Importance , Approaches to Measure and Detect , Challenges and Future Scope. 2015;5(10):791-8.

      [34] Popescu D. Reducing Ambiguities in Requirements Specifications via Automatically Created Object-Oriented Models. 2007;1.

      [35] Wang Y, Agichtein E. Query Ambiguity Revisited : Clickthrough Measures for Distinguishing Informational and Ambiguous Queries. Computational Linguistics. 2010(June):361-4.

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    Hayman Oo, K., Nordin, A., Ritahani Ismail, A., & Sulaiman, S. (2018). An Analysis of Ambiguity Detection Techniques for Software Requirements Specification (SRS). International Journal of Engineering & Technology, 7(2.29), 501-505. https://doi.org/10.14419/ijet.v7i2.29.13808