References
- Jitnah, D., J. Han, P. Steele. Software Requirements Engineering : An Overview 1 Introduction 2 Preliminaries. – Penins Sch. Comput. Inf. Technol. Monash. Univ., 1995, pp. 1-20.
- Ahmad, A., C. Feng, M. Khan, A. Khan, A. Ullah, S. Nazir, A. Tahir. A Systematic Literature Review on Using Machine Learning Algorithms for Software Requirements Identification on Stack Overflow. – Security and Communication Networks, Vol. 2020, 2020. DOI: 10.1155/2020/8830683.
- 830-1993, Recommended Practice for Software Requirements Specification. IEEE Computer Society. – Software Engineering Standard Committee of the IEEE Std Computer Society. Revision, 1998. p. 32.
- Femmer, H., D. M. Fernández, E. Juergens, M. Klose, I. Zimmer, J. Zimmer. Rapid Requirements Checks with Requirements Smells: Two Case Studies. – In: Proc. of 1st International Workshop on Rapid Continuous Software Engineering (RCoSE 2014), 2014, pp. 10-19. DOI: 10.1145/2593812.2593817.
- Sites, M., R. W. Selby. Software Engineering: Barry W. Boehm’s Lifetime Contributions to Software Development, Management, and Research. – Wiley-IEEE Press, 2007, pp. 1-13. DOI: 10.1109/9780470187562.ch8.
- Naeem, A., Z. Aslam, M. A. Shah. Analyzing Quality of Software Requirements; A Comparison Study on NLP Tools. – In: Proc. of 25th International Conference on Automation and Computing (ICAC’19), 2019, No September, pp. 1-6. DOI: 10.23919/IConAC.2019.8895182.
- Kocerka, Ī. J., Ī. Micha, Ī. A. Ga. Analysing Quality of Textual Requirements Using Natural Language Processing. – A Literature Review, 2018, pp. 876-880. DOI: 10.1109/MMAR.2018.8486143.
- Veizaga, A., S. Y. Shin, L. C. Briand. Automated Smell Detection and Recommendation in Natural Language Requirements. – IEEE Transactions on Software Engineering, 2024, pp. 1-26. DOI: 10.1109/TSE.2024.3361033.
- Xplore I. International Standard ISO/IEC/IEEE Systems and Software Engineering. – Engineering, Vol. 2018, 2018.
- Femmer, H. Reviewing Natural Language Requirements with Requirements Smells – A Research Proposal – Categories and Subject Descriptors. – In: Proc. of 11th International Doctoral Symposium on Empirical Software Engineering (IDoESE’13 at ESEM’13), 2013.
- Angara, J., S. Prasad, G. Sridevi. DevOPs Project Management Tools for Sprint Planning, Estimation and Execution Maturity. – Cybernetics and Information Technologies, Vol. 20, 2020, No 2, pp. 79-92.
- Mead, N. R., T. Stehney. Security Quality Requirements Engineering (SQUARE) Methodology. –- In: Proc. of 2005 Workshop on Software Engineering for Secure Systems – Building Trustworthy Applications (SESS’2005), 2005, pp. 1-7. DOI: 10.1145/1083200.1083214.
- Seki, Y., S. Hayashi, M. Saeki. Detecting Bad Smells in Use Case Descriptions. – In: Proc. of 27th IEEE International Requirements Engineering Conference (RE’19), 2019, pp. 98-108. DOI: 10.1109/RE.2019.00021.
- Alturayeif, N., I. Abdurahman, B. Faisal. Detection of Linguistic Bad Smells in GRL Models : An NLP Approach. – In: Proc. of ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C’23), 2023, pp. 318-327. DOI: 10.1109/MODELS-C59198.2023.00062.
- Chaithra, P., S. Nayak. Machine Learning Technique for Identifying Ambiguities of in Software Requirements. – Turkish Journal of Computer and Mathematics Education, Vol. 12, 2021, No 11, pp. 6852-6857. DOI: 10.17762/turcomat.v12i11.7159.
- Parra, E., C. Dimou, J. Llorens, V. Moreno, A. Fraga. A Methodology for the Classification of Quality of Requirements Using Machine Learning Techniques. – Information and Software Technology, Vol. 67, 2015, pp. 180-195. DOI: 10.1016/j.infsof.2015.07.006.
- Berhanu, F., E. Alemneh. Classification and Prioritization of Requirements Smells Using Machine Learning Techniques. – In: Proc. of International Conference on Information and Communication Technology for Development for Africa (ICT4DA’23), 2023, pp. 49-54. DOI: 10.1109/ICT4DA59526.2023.10302263.
- Al, A., S. Hamoud, A. Mohammad. Bad Smell Detection Using Machine Learning Techniques : A Systematic Literature Review. – Arabian Journal for Science and Engineering, Vol. 45, 2020, No 0123456789, pp. 2341-2369. DOI: 10.1007/s13369-019-04311-w.
- Sabir, F., F. Palma, G. Rasool, N. Moha. A Systematic Literature Review on the Detection of Smells and Their Evolution in Object-Oriented and Service-Oriented Systems. – Journal of Software: Practice and Experience, 2019, No July 2018, pp. 3-39. DOI: 10.1002/spe.2639.
- Aljedaani, W., A. Peruma, A. Aljohani, M. Alotaibi, M. W. Mkaouer, A. Ouni, C. D. Newman, A. Ghallab, S. Ludi. Test Smell Detection Tools: A Systematic Mapping Study. – In: Proc. of 25th International Conference on Evaluation and Assessment in Software Engineering, 2021, pp. 170-180. DOI: 10.1145/3463274.3463335.
- Mayra-Alejandra, Castillo-Motta, Rubén-Darío, Dorado-Córdoba, César-Jesús. Pardo-Calvache. Systematic Mapping of the Literature on Smells in Software Development Requirements. – Revista Facultad de Ingeniería, Vol. 32, 2023, No 63 pp. 0-3. DOI: 10.19053/01211129.v32.n63.2023.15233.
- Kitchenham, B., S. M. Charters. Guidelines for Performing Systematic Literature Reviews. – In: Software Engineering, 2007, No October 2021.
- Kitchenham, B., L. Madeyski, S. Member, D. Budgen. SEGRESS: Software Engineering Guidelines for Reporting Secondary Studies. – IEEE Transactions on Software Engineering, Vol. 49, 2023, No 3, pp. 1273-1298. DOI: 10.1109/TSE.2022.3174092.
- Sharma, T., D. Spinellis. Definitions of a Software Smell. https://zenodo.org/records/1066135
- Chatzigeorgiou, A., A. Manakos. Investigating the Evolution of Code Smells in Object-Oriented Systems. – Innovations in Systems and Software Engineering, Vol. 10, 2014, pp. 3-18. DOI: 10.1007/s11334-013-0205-z.
- Sharma, T., M. Fragkoulis, D. Spinellis. Does Your Configuration Code Smell ? – In: Proc. of 13th International Conference on Mining Software Repositories, 2016, pp. 189-200. DOI: 10.1145/2901739.2901761.
- Beck, K., J. Brant, W. Opdyke. Refactoring: Improving the Design of Existing Code. – In: Addison-Wesley Professional, 2018.
- Garcia, J., D. Popescu, G. Edwards, N. Medvidovic. Identifying Architectural Bad Smells. – In: Proc. of 13th European Conference on Software Maintenance and Reengineering, 2009, pp. 255-258. DOI: 10.1109/CSMR.2009.59.
- Bouhours, C., H. Leblanc, C. Percebois. Bad Smells in Design and Design Patterns. – The Journal of Object Technology, Vol. 8, 2010, No 3, pp. 43-63. DOI: 10.5381/jot.2009.8.3.c5.
- Rajkovic, K., E. Enoiu. NALABS: Detecting Bad Smells in Natural Language Requirements and Test Specifications. – ArXiv Preprint ArXiv:220205641, 2022, pp. 8-10. DOI: 10.48550/arXiv.2202.05641.
- Sharma, T., D. Spinellis. A Survey on Software Smells. – The Journal of Systems & Software, Vol. 138, 2018, pp. 158-173. DOI: 10.1016/j.jss.2017.12.034.
- Abuhassan, A. Software Smell Detection Techniques. – A Systematic Literature Review, 2021, No September 2019, pp. 1-48. DOI: 10.1002/smr.2320.
- Almeida, D., J. C. Campos, J. Saraiva, J. C. Silva. Towards a Catalog of Usability Smells. – In: Proc. of 30th Annual ACM Symposium on Applied Computing, 2014, pp. 175-181. DOI: 10.1145/2695664.2695670.
- Palma, F., N. Moha. A Study on the Taxonomy of Service Antipatterns. – In: Proc. of 2nd IEEE International Workshop on Patterns Promotion and Anti-Patterns Prevention (PPAP’15), 2015, pp. 5-8.
- Piveta, E. K., M. Hecht, A. Moreira, M. S. Pimenta, J. Araújo, P. Guerreiro, R. T. Price. Avoiding Bad Smells in Aspect-Oriented Software. – In: Proc. of 19th International Conference on Software Engineering and Knowledge Engineering (SEKE’07), 2007, pp. 81-86.
- Vale, G., E. Figueiredo, R. Abilio, H. Costa. Bad Smells in Software Product Lines: A Systematic Review. – In: Proc. of 8th Brazilian Symposium on Software Components, Architectures and Reuse (SBCARS’14), 2014, pp. 84-94. DOI: 10.1109/SBCARS.2014.21.
- Femmer, H., D. Méndez Fernández, S. Wagner, S. Eder. Rapid Quality Assurance with Requirements Smells. – Journal of Systems and Software, Vol. 123, 2017, pp. 190-213. DOI: 10.1016/j.jss.2016.02.047.
- Mu, F., L. Shi, W. Zhou, Y. Zhang, H. Zhao. NERO: A Text-Based Tool for Content Annotation and Detection of Smells in Feature Requests. – Proceedings of IEEE International Conference on Requirements Engineering, Vol. 2020-August, 2020, pp. 400-403. DOI: 10.1109/RE48521.2020.00056.
- Habib, M. K., S. Wagner, D. Graziotin. Detecting Requirements Smells with Deep Learning: Experiences, Challenges and Future Work. – Proceedings of IEEE International Conference on Requirements Engineering, Vol. 2021-September, 2021, pp. 153-156. DOI: 10.1109/REW53955.2021.00027.
- Gentili, Emanuele, D. Falessi. Characterizing Requirements Smells. – In: Proc. of International Conference on Product-Focused Software Process Improvement. Submitted on 17 April 2024. DOI: 10.48550/arXiv.2404.11106.
- Katasonov, A., M. Sakkinen. Requirements Quality Control : A Unifying Framework. – Requirements Engineering, 2006, pp. 42-57. DOI: 10.1007/s00766-005-0018-1.
- Mich, L., M. Franch, P. L. Novi Inverardi. Market Research for Requirements Analysis Using Linguistic Tools. – Requirements Engineering, Vol. 9, 2004, No 2, pp. 151-151. DOI: 10.1007/s00766-004-0195-3.
- Seki, Y., S. Hayashi, M. Saeki. Cataloging Bad Smells in Use Case Descriptions and Automating. – In: Proc. of 2019 IEEE 27th IEEE International Requirements Engineering Conference, 2022, No 5, pp. 849-863. DOI: 10.1587/transinf.2021KBP0008.
- Singh, G., J. C. Carver. A Systematic Literature Review to Identify and Classify Software Requirement Errors. – Information and Software Technology, Vol. 51, 2009, No 7, pp. 1087-1109. DOI: 10.1016/j.infsof.2009.01.004.
- Ge´nova, G., J. M. Fuentes, J. Llorens, O. Hurtado, V. Moreno. A Framework to Measure and Improve the Quality of Textual Requirements. – Requirement Engineering, Vol. 16, 2013, pp. 25-41. DOI: 10.1007/s00766-011-0134-z.
- Krogstie, J., O. I. Lindland. Towards a Deeper Understanding of Quality in Requirements Engineering Domain Quality Appropriateness Appropriatenes. – Model I Quality Language I Pragmatic I Interpretation Audience Appropriateness, Vol. 932, 1995, pp. 82-95. DOI: 10.1007/978-3-642-36926-1_7.
- Saavedra, R., L. Ballejos, M. Ale. Software Requirements Quality Evaluation: State of the Art and Research Challenges. – In: Proc. of 14th Argentine Symposium on Software Engineering, 2013, pp. 240-257.
- Özkan, D., A. Mishra. Agile Project Management Tools: A Brief Comprative View. – Cybernetics and Information Technologies, Vol. 19, 2019, No 4, pp. 17-25.
- Zakeri-Nasrabadi, M., S. Parsa. Natural Language Requirements Testability Measurement Based on Requirement Smells. – Neural Computing and Applications, 2024, pp. 1-35.
- Dochev, D., I. Hristov. On-the-Job e-Training – from Requirements to Design 1. – Cybernetics and Information Technologies, Vol. 3, 2003, No 2, pp. 45-54.
- Mayvan, B. B., A. Rasoolzadegan, A. J. Jafari. Bad Smell Detection Using Quality Metrics and Refactoring Opportunities. – Software: Evolution and Process, 2020, No December 2019, pp. 1-33. DOI: 10.1002/smr.2255.
- Oo, K. H., A. Nordin, A. R. Ismail, S. Sulaiman. An Analysis of Ambiguity Detection Techniques for Software Requirements Specification (SRS). – International Journal of Engineering & Technology, Vol. 7, 2018, pp. 501-505. DOI: 10.14419/ijet.v7i2.29.13808.
- Ferrari, A., G. Gori, B. Rosadini, I. Trotta, S. Bacherini, A. Fantechi, S. Gnesi. Detecting Requirements Defects with NLP Patterns: An Industrial Experience in the Railway Domain. – Empirical Software Engineering, Vol. 23, 2018, No 6, pp. 3684-3733. DOI: 10.1007/s10664-018-9596-7.
- Ezzini, S., S. Abualhaija, C. Arora, M. Sabetzadeh, L. C. Briand. Using Domain-Specific Corpora for Improved Handling of Ambiguity in Requirements. – In: Proc. of International Conference on Software Engineering, 2021, pp. 1485-1497. DOI: 10.1109/ICSE43902.2021.00133.
- Osama, M., A. Zaki-Ismail, M. Abdelrazek, J. Grundy, A. Ibrahim. Score-Based Automatic Detection and Resolution of Syntactic Ambiguity in Natural Language Requirements. – In: Proc. of IEEE International Conference on Software Maintenance and Evolution (ICSME’20), 2020, pp. 651-661. DOI: 10.1109/ICSME46990.2020.00067.
- Thitisathienkul, P., N. Prompoon. Quality Assessment Method for Software Requirements Specifications Based on Document Characteristics and Its Structure. – In: Proc. of 2nd International Conference on Trustworthy Systems and Their Applications (TSA’15), 2015, pp. 51-60. DOI: 10.1109/TSA.2015.19.
- Gleich, B., O. Creighton, L. Kof. Ambiguity Detection: Towards a Tool Explaining Ambiguity Sources. – Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 6182 LNCS, 2010, No May, pp. 218-232. DOI: 10.1007/978-3-642-14192-8_20.
- Wilmink, M., C. Bockisch. On the Ability of Lightweight Checks to Detect Ambiguity in Requirements Documentation. – Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 10153 LNCS, 2017, pp. 327-343. DOI: 10.1007/978-3-319-54045-0_23.
- Tjong, S. F., D. M. Berry. The Design of SREE – A Prototype Potential Ambiguity Finder for Requirements Specifications and Lessons Learned. – Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 7830 LNCS, 2013, pp. 80-95. DOI: 10.1007/978-3-642-37422-7_6.
- Kato, T., K. Tsuda. A Method of Ambiguity Detection in Requirement Specifications by Using a Knowledge Dictionary. – Procedia Computer Science, Vol. 207, 2022, pp. 1482-1489. DOI: 10.1016/j.procs.2022.09.205.
- Moreno, V., G. G. Orcid, E. Parra, A. Fraga. Application of Machine Learning Techniques to the Flexible Assessment and Improvement of Requirements Quality. 2020. DOI: 10.1007/s11219-020-09511-4.
- Hanisch, L. Detecting Vague Requirements with Machine Learning Detecting Vague Requirements with Machine Learning Detektion von Vagen Anforderungen mit Maschinellem Lernen. – Department of Informatics Technical University of Munich, 2020.
- Osman, M. H., M. F. Zaharin. Ambiguous Software Requirement Specification Detection: An Automated Approach. – In: Proc. of International Conference on Software Engineering, 2018, pp. 33-40. DOI: 10.1145/3195538.3195545.
- Yang, H., A. de Roeck, V. Gervasi, A. Willis, B. Nuseibeh. Analysing Anaphoric Ambiguity in Natural Language Requirements. – Requirements Engineering, Vol. 16, 2011, No 3, pp. 163-169. DOI: 10.1007/s00766-011-0119-y.
- Ezzini, S., S. Abualhaija, C. Arora, M. Sabetzadeh. Automated Handling of Anaphoric Ambiguity in Requirements: A Multi-Solution Study. – Proceedings International Conference on Software Engineering, Vol. 2022-May, 2022, pp. 187-199. DOI: 10.1145/3510003.3510157.
- Zhang, H., M. Ali, P. Tell. Identifying Relevant Studies in Software Engineering. – Information and Software Technology, Vol. 53, 2011, No 6, pp. 625-637. DOI: 10.1016/j.infsof.2010.12.010.
- Wohlin, C. Guidelines for Snowballing in Systematic Literature. – Studies and a Replication in Software Engineering, 2014. DOI: 10.1145/2601248.2601268.
- Cruzes, D. S., T. Dybå. Recommended Steps for Thematic Synthesis in Software Engineering. – In: Proc. of International Symposium on Empirical Software Engineering and Measurement, 2011, No 7491, pp. 275-284. DOI: 10.1109/esem.2011.36.
- Yang, B., Z. Xing, X. Xia, C. Chen, D. Ye, S. Li. UIS-Hunter: Detecting UI Design Smells in Android Apps. – Proceedings International Conference on Software Engineering, Vol. 1, 2021, No c, pp. 89-92. DOI: 10.1109/ICSE-Companion52605.2021.00043.
- Junker, M., S. Eder, L. Heinemann, C. Gmbh, R. Vaas, P. Braun, V. Ag. Hunting for Smells in Natural Language. – Tests No 1, pp. 4-7. DOI: 10.1109/ICSE.2013.6606682.
- Zhao, L., W. Alhoshan, A. Ferrari, K. J. Letsholo, M. A. Ajagbe, E. V. Chioasca, R. T. Batista-Navarro. Natural Language Processing (NLP) for Requirements Engineering : A Systematic Mapping Study. – ACM Computing Surveys (CSUR), Vol. 54, 2021, No 3, pp. 1-41. DOI: 10.1145/3444689.
- Kumawat, D., V. Jain. POS Tagging Approaches : A Comparison. – International Journal of Computer Applications, Vol. 118, 2015, No 6, pp. 32-38. DOI: 10.5120/20752-3148.
- Altinok, D. Mastering SpaCy – An End-to-End Practical Guide to Implementing NLP Applications Using the Python Ecosystem. Packt Publishing, Ltd., 2021.
- Toutanova, K., D. Klein, C. D. Manning. Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. – In: Proc. of HLT-NAACL 2003, 2003, No June, pp. 173-180. DOI: 10.3115/1073445.1073478.
- Gardner, M., J. Grus, M. Neumann, O. Tafjord, P. Dasigi, N. F. Liu, M. Peters, M. Schmitz, L. Zettlemoyer. AllenNLP: A Deep Semantic Natural Language Processing Platform. – In: Proc. of Workshop for NLP Open Source Software (NLP-OSS’17), 2017, pp. 3-8. DOI: 10.18653/v1/W18-2501.
- Eckart, R., D. C. Iryna. A Broad-Coverage Collection of Portable NLP Components for Building Shareable Analysis Pipelines. – Proceedings of Workshop on Open Infrastructures and Analysis Frameworks for HLT, Vol. 2, 2014, No 1, pp. 1-11.
- Cunningham, H., S. Computers, N. May, H. Cunningham. GATE, a General Architecture for Text Engineering GATE, a General Architecture for Text Engineering. – Computers and the Humanities, Vol. 36, 2002, No 2, pp. 223-254. DOI: 10.1023/A:1014348124664.
- Tsuruoka, Y., Y. Tateishi, J. D. Kim, T. Ohta, J. McNaught, S. Ananiadou, J. Tsujii. Developing a Robust Part-of-Speech Tagger for Biomedical Text. – Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3746 LNCS, 2005, pp. 382-392. DOI: 10.1007/11573036_36.
- Giuliano, C. jWeb1T : A Library for Searching the Web 1T 5. – Gram Corpus, 2011, pp. 2011.
- Zesch, T., C. Müller, I. Gurevych. Extracting Lexical Semantic Knowledge from Wikipedia and Wiktionary. – LREC, Vol. 8, 2007, No 2008, pp. 1646-1652.
- Yao, J. Automated Sentiment Analysis of Text Data with Automated Sentiment Analysis of Text Data with NLTK. 2019, pp. 0-8. DOI: 10.1088/1742-6596/1187/5/052020.
- Loper, E., S. Bird. NLTK: The Natural Language Toolkit. ArXiv Preprint Cs/0205028, 2002.
- Hornby, A. S. Oxford Advanced Learner’s Dictionary of Current English. – Oxford Univer Press.
- Fabbrini, F., M. Fusani, S. Gnesi, G. Lami. An Automatic Quality Evaluation for Natural Language Requirements. – In: Proc. of 7th International Workshop on Requirements Engineering: Foundation for Software Quality REFSQ, 2001, No March 2014.
- Lami, G., S. Gnesi, F. Fabbrini, M. Fusani, G. Trentanni. An Automatic Tool for the Analysis of Natural Language Requirements. – Informe Técnico, CNR Information Science and Technology Institute, 2004.
- Verma, Tanu Gaur D. Tokenization and Filtering Process in RapidMiner. – International Journal of Applied Information Systems, Vol. 7, 2014, No 2, pp.16-18.
- Read, J., R. Dridan, S. Oepen, L. J. Solberg. Sentence Boundary Detection: A Long Solved Problem ? – Proceedings of COLING 2012: Posters. No December 2012, pp. 985-994.
- Qi, P., Y. Zhang, Y. Zhang, J. Bolton, C. D. Manning. Stanza: A Python Natural Language Processing Toolkit for Many Human Languages. – ArXiv Preprint ArXiv:200307082, 2020.
- Asano, K., S. Hayach, M. Saeki. Detecting Bad Smells of Refinement in Goal-Oriented Requirements Analysis. – Advances in Conceptual Modeling. – In: Proc. of ER 2017 Workshops AHA, MoBiD, MREBA, OntoCom, and QMMQ, 2017, pp. 122-132. DOI: 10.1007/978-3-319-70625-2.
- Manning, C. D., M. Surdeanu, J. Bauer, J. Finkel, S. J. Bethard, D. McClosky. The Stanford CoreNLP Natural Language Processing Toolkit. – In: Proc. of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2014, pp. 55-60.
- Miller, G. A. WordNet : A Lexical Database for English. – Communications of the ACM, Vol. 38, 1995, No 11, pp. 39-41. DOI: 10.1145/219717.219748.
- Hideki, Shima. WordNet Similarity for Java Relatedness – Similarity Algorithms, pp. 2-7.
- Briand, L., M. Alferez, D. Torre, M. Sabetzadeh. On Systematically Building a Controlled Natural. – Empirical Software Engineering, 2021. DOI: 10.1007/s10664-021-09956-6.
- Deissenboeck, F., L. Heinemann, B. Hummel, E. Juergens. Flexible Architecture Conformance Assessment with ConQAT. – ICSE’10, 2010, No 1.
- Wilson, W. M., W. M. Wilson, L. H. Rosenberg, L. Rosenberg, L. E. Hyatt. Automated Analysis of Requirement Specifications. – In: Proc. of 19th International Conference on Software Engineering, 1997, pp. 161-171. DOI: 10.1145/253228.253258.
- Pedregosa, F., G. Varoquaux, A. Gramfort, et al. Scikit-Learn: Machine Learning in Python. – Journal of Machine Learning Research, Vol. 12, 2011, pp. 2825-2830.
- Hall, M., H. National, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I. H. Witten. The WEKA Data Mining Software. – An Update, Vol. 11, No 1, pp. 10-18. DOI: 10.1145/1656274.1656278.
- Levy, R., G. Andrew. Tregex and Tsurgeon: Tools for Querying and Manipulating Tree Data Structures. – LREC, 2005.
- Zhou, X., S. Li. A Map of Threats to Validity of Systematic. – Literature Reviews in Software Engineering, 2016, pp. 153-160. DOI: 10.1109/APSEC.2016.62.
- Ampatzoglou, A., S. Bibi, P. Avgeriou, M. Verbeek, A. Chatzigeorgiou. Identifying, Categorizing and Mitigating Threats to Validity in Software Engineering Secondary Studies. 2016. DOI: 10.1016/j.infsof.2018.10.006.
- Stapic, Z., E. G. López, A. G. Cabot, L. Ortega de M., V. Strahonja. Performing Systematic literature Review in Software Engineering. – In: Proc. of Central European Conference on Information and Intelligent Systems, 2012, No 2012, pp. 442-493. DOI: 10.1145/1134285.1134500.
- Wohlin, C., P. Runeson, Martin H¨ost, M. C. Ohlsson, B. Regnell, A. Wessl´en. Experimentation in Software Engineering. Springer Science & Business Media, 2012.
- Farrugia, P., B. A. Petrisor, F. Farrokhyar, M. Bhandari. Practical Tips for Surgical Research: Research Questions, Hypotheses and Objectives. – Canadian Journal of Surgery Journal Canadien de Chirurgie, Vol. 53, 2010, No 4, pp. 278-281.
- Beer, A., M. Felderer. Initial Investigations on the Influence of Requirement Smells on Test-Case Design. – In: Proc. of 25th IEEE International Requirements Engineering Conference Workshops (REW’17), 2017, pp. 323-326. DOI: 10.1109/REW.2017.43.
- Pei, Z., L. Liu, C. Wang, J. Wang. Requirements Engineering for Machine Learning : A Review and Reflection. – In: Proc. of 30th IEEE International Requirements Engineering Conference Workshops (REW’22), 2022, pp. 166-175. DOI: 10.1109/REW56159.2022.00039.
- Rath, M., P. Rempel, M. Patrick. The IlmSeven Dataset. – In: Proc. of 25th IEEE International Requirements Engineering Conference, 2017, pp. 516-519.
- Sommerville, I. Software Engineering. Ninth Edit. Addison-Wesley, 2011.
- Aversano, L., U. Carpenito, M. Iammarino. An Empirical Study on the Evolution of Test Smell. – In: Proc. of 42nd ACM/IEEE International Conference on Software Engineering: Companion, ICSE-Companion, 2020, pp. 149-151. DOI: 10.1145/3377812.3382176.
