Skip to main content
SpringerLink
Log in

Code Smells Detection Using Artificial Intelligence Techniques: A Business-Driven Systematic Review

  • Chapter
  • First Online:
Developments in Information & Knowledge Management for Business Applications

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 377))

Abstract

Context Code smells in the software systems are indications that usually correspond to deeper problems that can negatively influence software quality characteristics. This review is a part of a R&D project aiming to improve the existing codebeat platform that help developers to avoid code smells and deliver quality code. Objective This study aims to identify and investigate the current state of the art with respect to: (1) predictors used in prediction models to detect code smells, (2) machine learning/artificial intelligence (ML/AI) methods used in prediction models to detect code smells, (3) code smells analyzed in scientific literature. Our secondary objectives were to identify (4) data sets and projects used in research papers to predict code smells, (5) performance measures used to assess prediction models and (6) improvement ideas with regard to code smell detection using ML/AI. Method We conducted a systematic review using a database search in Scopus and evaluated it using the quasi-gold standard procedure to identify relevant studies. In the data sheet used to obtain data from publications we factor research questions into finer-grained ones, which are then answered on a per-publication basis. Those are then merged over a set of publications using an automated script to obtain answers to the posed research questions. Results We have identified 45 primary studies relevant to the primary objectives of this research. The results show the prediction capability of the ML/AI techniques for predicting code smells. Conclusion Only a few smells—Blob, Feature Envy, Long Method and Data Class—have received the vast majority of interest in research community. The usage of deep learning techniques is increasing. Most researchers still use source code metrics as predictors. Precision, recall and F-measure are the go-to performance metrics. There seems to be a need for modern reference data/projects sets that reflect modern constructs of programming languages. We identified various promising paths of research that have the potential to advance the state of the art in the area of code smells prediction.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    codequest.com.

  2. 2.

    codebeat.co.

  3. 3.

    More details can be found at https://service.elsevier.com/app/answers/detail/a_id/11213/supporthub/scopus/#tips and https://service.elsevier.com/app/answers/detail/a_id/11236/kw/all%20fields/supporthub/scopus/.

  4. 4.

    Apply only if recall does not reach required threshold.

  5. 5.

    Zhang et al. [29] suggest that a sensitivity (recall) threshold (i.e., a completeness target) of between 70% and 80% might be used to decide whether to go to Step 3 (and to refine the search terms) or whether to proceed to the next stage of the review.

  6. 6.

    We focus on the main full papers research tracks of the conferences, and do not cover collocated conferences or workshops.

  7. 7.

    https://github.com/AlDanial/cloc.

References

  1. Al-Shaaby, A., Aljamaan, H., Alshayeb, M.: Bad smell detection using machine learning techniques: a systematic literature review. Arabian J. Sci. Eng. 45, 2341–2369 (2020). https://doi.org/10.1007/s13369-019-04311-w

    Article  Google Scholar 

  2. Azeem, M.I., Palomba, F., Shi, L., Wang, Q.: Machine learning techniques for code smell detection: a systematic literature review and meta-analysis. Inf. Softw. Technol. 108, 115 – 138 (2019). https://doi.org/10.1016/j.infsof.2018.12.009

  3. Buenen, M., Muthukrishnan, G.: World quality report 2016–17. Technical report, Sogeti and Hewlett Packard Enterprise, Capgemini (2016)

    Google Scholar 

  4. Caram, F., de Oliveira Rodrigues, B.R., Campanelli, A., Silva Parreiras, F.: Machine learning techniques for code smells detection: a systematic mapping study. Int. J. Softw. Eng. Knowl. Eng. 29, 285–316 (2019). http://orcid.org/10.1142/S021819401950013X

  5. Chen, B., Jiang, Z.M.: Characterizing and detecting anti-patterns in the logging code. In: Proceedings—2017 IEEE/ACM 39th International Conference on Software Engineering, ICSE 2017, pp. 71–81 (2017). https://doi.org/10.1109/ICSE.2017.15

  6. Di Nucci, D., Palomba, F., Tamburri, D.A., Serebrenik, A., De Lucia, A.: Detecting code smells using machine learning techniques: Are we there yet? In: 2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 612–621 (2018). https://doi.org/10.1109/SANER.2018.8330266

  7. Dieste, O., Grimán, A., Juristo, N.: Developing search strategies for detecting relevant experiments. Empirical Softw. Eng. 14(5), 513–539 (2009). http://orcid.org/10.1109/ESEM.2007.19

  8. Dybå, T., Dingsøyr, T.: Empirical studies of agile software development: a systematic review. Inf. Softw. Technol. 50(9–10), 833–859 (2008). http://orcid.org/10.1016/j.infsof.2008.01.006

  9. Fleiss, J.L.: Measuring nominal scale agreement among many raters. Psychol. Bull. 76(5), 378–382 (1971). http://orcid.org/10.1037/h0031619

  10. Fontana, F.A., Pigazzini, I., Roveda, R., Zanoni, M.: Automatic detection of instability architectural smells. In: Proceedings—2016 IEEE International Conference on Software Maintenance and Evolution, ICSME 2016, pp. 433–437 (2017). https://doi.org/10.1109/ICSME.2016.33

  11. Fontana, F.A., Zanoni, M.: Code smell severity classification using machine learning techniques. Knowl. -Based Syst. 128, 43–58 (2017). http://orcid.org/10.1016/j.knosys.2017.04.014

  12. Fowler, M., Beck, K., Brant, J., Opdyke, W., Roberts, D.: Refactoring: Improving the Design of Existing Code. Addison-Wesley, Boston, MA, USA (1999)

    Google Scholar 

  13. Gartner: Gartner says worldwide software market grew 4.8 percent in 2013 (2014)

    Google Scholar 

  14. Kitchenham, B., Budgen, D., Brereton, P.: Evidence-Based Software Engineering and Systematic Reviews. CRC Press (2016). http://orcid.org/10.1007/11767718\_3

    Google Scholar 

  15. Madeyski, L., Lewowski, T.: MLCQ: Industry-relevant code smell data set. In: Proceedings of the Evaluation and Assessment in Software Engineering, EASE ’20, pp. 342–347. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3383219.3383264

  16. Malhotra, R.: A systematic review of machine learning techniques for software fault prediction. Appl. Softw. Comput. 27, 504–518 (2015). http://orcid.org/10.1016/j.asoc.2014.11.023

  17. Palomba, F., Bavota, G., Di Penta, M., Fasano, F., Oliveto, R., Lucia, A.: On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation. Empirical Softw. Eng. pp. 1–34 (2017). https://doi.org/10.1007/s10664-017-9535-z

  18. Palomba, F., Di Nucci, D., Panichella, A., Zaidman, A., De Lucia, A.: Lightweight detection of android-specific code smells: the adoctor project. In: SANER 2017—24th IEEE International Conference on Software Analysis, Evolution, and Reengineering, pp. 487–491 (2017). https://doi.org/10.1109/SANER.2017.7884659

  19. Palomba, F., Di Nucci, D., Tufano, M., Bavota, G., Oliveto, R., Poshyvanyk, D., De Lucia, A.: Landfill: an open dataset of code smells with public evaluation. In: 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories, pp. 482–485 (2015). https://doi.org/10.1109/MSR.2015.69

  20. Palomba, F., Panichella, A., Zaidman, A., Oliveto, R., De Lucia, A.: The scent of a smell: an extensive comparison between textual and structural smells. IEEE Transa. Softw. Eng. (2017). http://orcid.org/10.1109/TSE.2017.2752171

  21. Romano, S., Scanniello, G., Sartiani, C., Risi, M.: A graph-based approach to detect unreachable methods in java software. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, SAC ’16, p. 1538–1541. Association for Computing Machinery, New York, NY, USA (2016). https://doi.org/10.1145/2851613.2851968

  22. Santos, J.A.M., Rocha-Junior, J.B., Prates, L.C.L., do Nascimento, R.S., Freitas, M.F., de Mendonca, M.G.: A systematic review on the code smell effect. J. Syst. Softw. 144, 450 – 477 (2018). https://doi.org/10.1016/j.jss.2018.07.035

  23. Sharma, T., Spinellis, D.: A survey on software smells. J. Syst. Softw. 138, 158–173 (2018). https://doi.org/10.1016/j.jss.2017.12.034

  24. Singh, S., Kaur, S.: A systematic literature review: Refactoring for disclosing code smells in object oriented software. Ain Shams Eng. J. (2017). https://doi.org/10.1016/j.asej.2017.03.002

  25. Tempero, E., Anslow, C., Dietrich, J., Han, T., Li, J., Lumpe, M., Melton, H., Noble, J.: Qualitas corpus: a curated collection of java code for empirical studies. In: 2010 Asia Pacific Software Engineering Conference (APSEC2010), pp. 336–345 (2010). http://dx.doi.org/10.1109/APSEC.2010.46

  26. Wasylkowski, A., Zeller, A., Lindig, C.: Detecting object usage anomalies. In: 6th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, ESEC/FSE 2007, pp. 35–44 (2007). https://doi.org/10.1145/1287624.1287632

  27. Wen, J., Li, S., Lin, Z., Hu, Y., Huang, C.: Systematic literature review of machine learning based software development effort estimation models. Inform. Softw. Technol. 54(1), 41–59 (2012). http://orcid.org/10.1016/j.infsof.2011.09.002

  28. Wohlin, C.: Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering EASE’14 (2014). https://doi.org/10.1145/2601248.2601268

  29. Zhang, H., Babar, M.A., Tell, P.: Identifying relevant studies in software engineering. Inf. Softw. Technol. 53(6), 625–637 (2011). http://orcid.org/10.1016/j.infsof.2010.12.010

  30. Zhang, M., Hall, T., Baddoo, N.: Code Bad Smells: a review of current knowledge. J. Softw. Mainten. Evolut. Res. Pract. 23(3), 179–202 (2011). http://orcid.org/10.1002/smr.521

Systematic Literature Review References

  1. Amorim, L., Costa, E., Antunes, N., Fonseca, B., Ribeiro, M.: Experience report: evaluating the effectiveness of decision trees for detecting code smells. In: 2015 IEEE 26th International Symposium on Software Reliability Engineering, ISSRE 2015, pp. 261–269 (2016). https://doi.org/10.1109/ISSRE.2015.7381819

  2. Barbez, A., Khomh, F., Guéhéneuc, Y.G.: A machine-learning based ensemble method for anti-patterns detection. J. Syst. Softw. 161, (2020). https://doi.org/10.1016/j.jss.2019.110486

  3. Barbez, A., Khomh, F., Gueheneuc, Y.G.: Deep learning anti-patterns from code metrics history. In: Proceedings—2019 IEEE International Conference on Software Maintenance and Evolution, ICSME 2019, pp. 114–124 (2019). https://doi.org/10.1109/ICSME.2019.00021

  4. Boussaa, M., Kessentini, W., Kessentini, M., Bechikh, S., Ben Chikha, S.: Competitive coevolutionary code-smells detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8084 LNCS, 50–65 (2013). https://doi.org/10.1007/978-3-642-39742-4_6

  5. Bryton, S., Brito e Abreu, F., Monteiro, M.: Reducing subjectivity in code smells detection: Experimenting with the long method. In: Proceedings—7th International Conference on the Quality of Information and Communications Technology, QUATIC 2010, pp. 337–342 (2010). https://doi.org/10.1109/QUATIC.2010.60

  6. Chen, Z., Chen, L., Ma, W., Zhou, X., Zhou, Y., Xu, B.: Understanding metric-based detectable smells in python software: a comparative study. Inf. Softw. Technol. 94, 14–29 (2018). http://orcid.org/10.1016/j.infsof.2017.09.011

  7. Fakhoury, S., Arnaoudova, V., Noiseux, C., Khomh, F., Antoniol, G.: Keep it simple: Is deep learning good for linguistic smell detection? In: 25th IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2018—Proceedings, vol. 2018-March, pp. 602–611 (2018). https://doi.org/10.1109/SANER.2018.8330265

  8. Fontana, F.A., Zanoni, M., Marino, A., Mäntylä, M.V.: Code smell detection: towards a machine learning-based approach. In: IEEE International Conference on Software Maintenance, ICSM, pp. 396–399 (2013). https://doi.org/10.1109/ICSM.2013.56

  9. Fontana, F.A., Mäntylä, M.V., Zanoni, M., Marino, A.: Comparing and experimenting machine learning techniques for code smell detection. Empirical Softw. Eng. 21(3), 1143–1191 (2016). http://orcid.org/10.1007/s10664-015-9378-4

  10. Fu, S., Shen, B.: Code bad smell detection through evolutionary data mining. In: International Symposium on Empirical Software Engineering and Measurement, vol. 2015-November, pp. 41–49 (2015). 10.1109/ESEM.2015.7321194

    Google Scholar 

  11. Gauthier, F., Merlo, E.: Semantic smells and errors in access control models: a case study in PHP. In: Proceedings—International Conference on Software Engineering, pp. 1169–1172 (2013). https://doi.org/10.1109/ICSE.2013.6606670

  12. Grodzicka, H., Ziobrowski, A., Łakomiak, Z., Kawa, M., Madeyski, L.: Code smell prediction employing machine learning meets emerging Java Language constructs. In: Poniszewska-Marańda, A., Kryvinska, N., Jarząbek, S., Madeyski, L. (eds.) Data-Centric Business and Applications: Towards Software Development, vol. 40 of book series Lecture Notes on Data Engineering and Communications Technologies, pp. 137–167. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-34706-2_8

  13. Guggulothu, T., Moiz, S.A.: Code smell detection using multi-label classification approach. Softw. Qual. J. (2020). https://doi.org/10.1007/s11219-020-09498-y

  14. Guo, X., Shi, C., Jiang, H.: Deep semantic-based feature envy identification. ACM International Conference Proceeding Series (2019). https://doi.org/10.1145/3361242.3361257

  15. Hadj-Kacem, M., Bouassida, N.: A hybrid approach to detect code smells using deep learning. In: ENASE 2018—Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering, vol. 2018-March, pp. 137–146 (2018). https://doi.org/10.5220/0006709801370146

  16. Hadj-Kacem, M., Bouassida, N.: Deep representation learning for code smells detection using variational auto-encoder. In: Proceedings of the International Joint Conference on Neural Networks, vol. 2019-July (2019). https://doi.org/10.1109/IJCNN.2019.8851854

  17. Hassaine, S., Khomh, F., Guéhéneucy, Y.G., Hamel, S.: IDS: An immune-inspired approach for the detection of software design smells. In: Proceedings—7th International Conference on the Quality of Information and Communications Technology, QUATIC 2010, pp. 343–348 (2010). https://doi.org/10.1109/QUATIC.2010.61

  18. Hozano, M., Antunes, N., Fonseca, B., Costa, E.: Evaluating the accuracy of machine learning algorithms on detecting code smells for different developers. In: Proceedings of the 19th International Conference on Enterprise Information Systems, Vol. 2: ICEIS, pp. 474–482. INSTICC, SciTePress (2017). https://doi.org/10.5220/0006338804740482

  19. James Benedict Felix, S., Vinod, V.: Design and analysis of improvised genetic algorithm with particle swarm optimization for code smell detection. Int. J. Innov. Technol. Explor. Eng. 9(1), 5327–5330 (2019). https://doi.org/10.35940/ijitee.A5328.119119

  20. Jesudoss, A., Maneesha, S., Lakshmi Naga Durga, T.: Identification of code smell using machine learning. In: 2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019, pp. 54–58 (2019). https://doi.org/10.1109/ICCS45141.2019.9065317

  21. Karaduzovic-Hadziabdic, K., Spahic, R.: Comparison of machine learning methods for code smell detection using reduced features. In: UBMK 2018—3rd International Conference on Computer Science and Engineering, pp. 670–672 (2018). https://doi.org/10.1109/UBMK.2018.8566561

  22. Kaur, A., Jain, S., Goel, S.: A support vector machine based approach for code smell detection. In: Proceedings—2017 International Conference on Machine Learning and Data Science, MLDS 2017, vol. 2018-January, pp. 9–14 (2018). https://doi.org/10.1109/MLDS.2017.8

  23. Kaur, A., Jain, S., Goel, S.: SP-J48: a novel optimization and machine-learning-based approach for solving complex problems: special application in software engineering for detecting code smells. Neural Comput. Appl. (2019). http://orcid.org/10.1007/s00521-019-04175-z

  24. Kessentini, W., Kessentini, M., Sahraoui, H., Bechikh, S., Ouni, A.: A cooperative parallel search-based software engineering approach for code-smells detection. IEEE Trans. Softw. Eng. 40(9), 841–861 (2014). http://orcid.org/10.1109/TSE.2014.2331057

  25. Kessentini, M., Ouni, A.: Detecting android smells using multi-objective genetic programming. In: 2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft), pp. 122–132 (2017). https://doi.org/10.1109/MOBILESoft.2017.29

  26. Khomh, F., Vaucher, S., Guéehéneuc, Y.G., Sahraoui, H.: A Bayesian approach for the detection of code and design smells. In: Proceedings—International Conference on Quality Software, pp. 305–314 (2009). https://doi.org/10.1109/QSIC.2009.47

  27. Kiyak, E.O., Birant, D., Birant, K.U.: Comparison of multi-label classification algorithms for code smell detection. In: 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019—Proceedings (2019). https://doi.org/10.1109/ISMSIT.2019.8932855

  28. Kreimer, J.: Adaptive detection of design flaws. Electronic Notes in Theoretical Computer Science 141(4 SPEC. ISS.), 117–136 (2005). https://doi.org/10.1016/j.entcs.2005.02.059

  29. Liu, H., Jin, J., Xu, Z., Bu, Y., Zou, Y., Zhang, L.: Deep learning based code smell detection. IEEE Trans. Softw. Eng. (2019). http://orcid.org/10.1109/TSE.2019.2936376

  30. Liu, H., Liu, Q., Niu, Z., Liu, Y.: Dynamic and automatic feedback-based threshold adaptation for code smell detection. IEEE Trans. Softw. Eng. 42(6), 544–558 (2016). http://orcid.org/10.1109/TSE.2015.2503740

  31. Liu, H., Xu, Z., Zou, Y.: Deep learning based feature envy detection. In: ASE 2018—Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, pp. 385–396 (2018). https://doi.org/10.1145/3238147.3238166

  32. Maiga, A., Ali, N., Bhattacharya, N., Sabané, A., Guéneuc, Y.G., Aimeur, E.: SMURF: A SVM-based incremental anti-pattern detection approach. In: Proceedings—Working Conference on Reverse Engineering, WCRE, pp. 466–475 (2012). https://doi.org/10.1109/WCRE.2012.56

  33. Mansoor, U., Kessentini, M., Maxim, B.R., Deb, K.: Multi-objective code-smells detection using good and bad design examples. Softw. Qual. J. 25(2), 529–552 (2017). http://orcid.org/10.1007/s11219-016-9309-7

  34. Merzah, B.M.: Software quality prediction using data mining techniques. In: 2019 International Conference on Information and Communications Technology, ICOIACT 2019, pp. 394–397 (2019). https://doi.org/10.1109/ICOIACT46704.2019.8938487

  35. Mkaouer, M.W.: Interactive code smells detection: an initial investigation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9962 LNCS, 281–287 (2016). https://doi.org/10.1007/978-3-319-47106-8_24

  36. Ocariza, F.S., Pattabiraman, K., Mesbah, A.: Detecting unknown inconsistencies in web applications. In: ASE 2017—Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering, pp. 566–577 (2017). https://doi.org/10.1109/ASE.2017.8115667

  37. Özkalkan, Z., Aydin, K.S., Tetik, H.Y., Belen Saglam, R.: Automatic detection of feature envy using machine learning techniques. In: CEUR Workshop Proceedings, vol. 2201 (2018). http://ceur-ws.org/Vol-2201/UYMS_2018_paper_80.pdf

  38. Palomba, F., Bavota, G., Di Penta, M., Oliveto, R., De Lucia, A., Poshyvanyk, D.: Detecting bad smells in source code using change history information. In: 2013 28th IEEE/ACM International Conference on Automated Software Engineering, ASE 2013—Proceedings, pp. 268–278 (2013). https://doi.org/10.1109/ASE.2013.6693086

  39. Palomba, F.: Alternative sources of information for code smell detection: postcards from far away. In: Proceedings—2016 IEEE International Conference on Software Maintenance and Evolution, ICSME 2016, pp. 636–640 (2017). https://doi.org/10.1109/ICSME.2016.26

  40. Palomba, F.: Textual analysis for code smell detection. Proc. Int. Conf. Softw. Eng. 2, 769–771 (2015). http://orcid.org/10.1109/ICSE.2015.244

  41. Pradel, M., Heiniger, S., Gross, T.R.: Static detection of brittle parameter typing. In: Proceedings of the 2012 International Symposium on Software Testing and Analysis, ISSTA 2012, p. 265–275. Association for Computing Machinery, New York, NY, USA (2012). https://doi.org/10.1145/2338965.2336785

  42. Rubin, J., Henniche, A.N., Moha, N., Bouguessa, M., Bousbia, N.: Sniffing android code smells: an association rules mining-based approach. In: Proceedings—2019 IEEE/ACM 6th International Conference on Mobile Software Engineering and Systems, MOBILESoft 2019, pp. 123–127 (2019). https://doi.org/10.1109/MOBILESoft.2019.00025

  43. Sahin, D., Kessentini, M., Bechikh, S., Deb, K.: Code-smell detection as a bilevel problem. ACM Trans. Softw. Eng. Methodol. 24(1) (2014). https://doi.org/10.1145/2675067

  44. Sharma, P., Kaur, E.A.: Design of testing framework for code smell detection (OOPS) using BFO algorithm. Int. J. Eng. Technol. (UAE) 7(2.27 Special Issue 27), 161–166 (2018). https://doi.org/10.14419/ijet.v7i2.27.14635

  45. Tummalapalli, S., Kumar, L., Neti, L.B.M.: An empirical framework for web service anti-pattern prediction using machine learning techniques. In: IEMECON 2019—9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference, pp. 137–143 (2019). https://doi.org/10.1109/IEMECONX.2019.8877008

Download references

Acknowledgements

This research was partly financed by Polish National Centre for Research and Development grant POIR.01.01.01-00-0792/16: “Codebeat—wykorzystanie sztucznej inteligencji w statycznej analizie jakości oprogramowania.”

Author information

Authors and Affiliations

  1. Department of Applied Informatics, Wroclaw University of Science and Technology, Wroclaw, Poland

    Tomasz Lewowski & Lech Madeyski

Authors
  1. Tomasz Lewowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lech Madeyski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tomasz Lewowski .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Faculty of Management, Comenius University, Bratislava, Slovakia

    Natalia Kryvinska

  2. Institute of Information Technology, Lodz University of Technology, Łódź, Poland

    Aneta Poniszewska-Marańda

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Lewowski, T., Madeyski, L. (2022). Code Smells Detection Using Artificial Intelligence Techniques: A Business-Driven Systematic Review. In: Kryvinska, N., Poniszewska-Marańda, A. (eds) Developments in Information & Knowledge Management for Business Applications . Studies in Systems, Decision and Control, vol 377. Springer, Cham. https://doi.org/10.1007/978-3-030-77916-0_12

Download citation

Publish with us

Policies and ethics

Search

Navigation