Abstract
The challenge of effective refactoring in the software development cycle brought forward the need to develop automated defect prediction models. Among many existing indicators of bad code, code smells have attracted particular interest of both the research community and practitioners in recent years. In this paper, we describe the current state-of-the-art in the field of bug prediction with the use of code smells and attempt to identify areas requiring further research. To achieve this goal, we conducted a systematic literature review of 27 research papers published between 2006 and 2019. For each paper, we (i) analysed the reported relationship between smelliness and bugginess, as well as (ii) evaluated the performance of code smell data used as a defect predictor in models developed using machine learning techniques. Our investigation confirms that code smells are both positively correlated with software defects and can positively influence the performance of fault detection models. However, not all types of smells and smell-related metrics are equally useful. God Class, God Method, Message Chains smells and Smell intensity metric stand out as particularly effective. Smells such as Inappropriate Intimacy, Variable Re-assign, Clones, Middle Man or Speculative Generality require further research to confirm their contribution. Metrics describing the introduction and evolution of anti-patterns in code present a promising opportunity for experimentation.
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Papers analysed in the systematic literature review
Aman H (2012) An empirical analysis on fault-proneness of well-commented modules. In: Proceedings - 2012 4th international workshop on empirical software engineering in practice, IWESEP 2012, pp 3–9. https://doi.org/10.1109/IWESEP.2012.12
Aman H, Amasaki S, Sasaki T, Kawahara M (2014) Empirical analysis of fault-proneness in methods by focusing on their comment lines. In: Proceedings—Asia-pacific software engineering conference, APSEC. https://doi.org/10.1109/APSEC.2014.93
Aman H, Amasaki S, Sasaki T, Kawahara M (2015) Lines of comments as a noteworthy metric for analyzing fault-proneness in methods. IEICE Trans Inf Syst. https://doi.org/10.1587/transinf.2015EDP7107
Bán D, Ferenc R (2014) Recognizing antipatterns and analyzing their effects on software maintainability, vol 8583 LNCS. https://doi.org/10.1007/978-3-319-09156-3_25
D’Ambros M, Bacchelli A, Lanza M (2010) On the impact of design flaws on software defects. In: Proceedings - international conference on quality software 1:23–31. https://doi.org/10.1109/QSIC.2010.58
Hall T, Zhang M, Bowes D, Sun Y (2014) Some code smells have a significant but small effect on faults. ACM Trans Softw Eng Methodol. https://doi.org/10.1145/2629648
Izurieta C, Seaman C, Cai Y, Shull F, Zazworka N, Wong S, Vetro’ A (2013) Comparing four approaches for technical debt identification. Softw Qual J 22(3):403–426. https://doi.org/10.1007/s11219-013-9200-8
Jaafar F, Guéhéneuc Y, Hamel S, Khomh F (2013) Mining the relationship between anti-patterns dependencies and fault-proneness. In: 2013 20th working conference on reverse engineering (WCRE), pp 351–360. https://doi.org/10.1109/WCRE.2013.6671310
Jaafar F, Khomh F, Gueheneuc YG, Zulkernine M (2014) Anti-pattern mutations and fault-proneness. In: Proceedings - international conference on quality software, pp 246–255. https://doi.org/10.1109/QSIC.2014.45
Jaafar F, Lozano A, Gueheneuc YG, Mens K (2017) On the analysis of co-occurrence of anti-patterns and clones. In: Proceedings - 2017 ieee international conference on software quality, reliability and security, QRS 2017. https://doi.org/10.1109/QRS.2017.38
Kaur K, Kaur P (2017) Evaluation of sampling techniques in software fault prediction using metrics and code smells. In: 2017 international conference on advances in computing, communications and informatics, ICACCI 2017, vol 2017-Janua. IEEE, pp 1377–1386. https://doi.org/10.1109/ICACCI.2017.8126033
Khomh F, Penta MD, Guéhéneuc YG, Antoniol G (2012) An exploratory study of the impact of antipatterns on class change- and fault-proneness. Empir Softw Eng 17(3):243–275. https://doi.org/10.1007/s10664-011-9171-y
Li W, Shatnawi R (2007) An empirical study of the bad smells and class error probability in the post-release object-oriented system evolution. J Syst Softw 80(7):1120–1128. https://doi.org/10.1016/j.jss.2006.10.018
Marinescu R, Marinescu C (2011) Are the clients of flawed classes (also) defect prone? In: Proceedings - 11th IEEE international working conference on source code analysis and manipulation, SCAM 2011, pp 65–74. https://doi.org/10.1109/SCAM.2011.9
Olbrich SM, Cruzes DS, Sjoøberg DI (2010) Are all code smells harmful? a study of god classes and brain classes in the evolution of three open source systems. In: IEEE international conference on software maintenance. ICSM. https://doi.org/10.1109/ICSM.2010.5609564
Palomba F, Bavota G, Penta MD, Fasano F, Oliveto R, Lucia AD (2018) On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation. Empir Softw Eng. https://doi.org/10.1007/s10664-017-9535-z
Palomba F, Zanoni M, Fontana FA, De Lucia A, Oliveto R (2017) Smells like teen spirit: improving bug prediction performance using the intensity of code smells. In: Proceedings - 2016 IEEE international conference on software maintenance and evolution. ICSME 2016, pp. 244–255. https://doi.org/10.1109/ICSME.2016.27
Palomba F, Zanoni M, Fontana FA, Lucia AD, Oliveto R (2019) Toward a smell-aware bug prediction model. IEEE Trans Softw Eng 45(2):194–218. https://doi.org/10.1109/TSE.2017.2770122
Rahman F, Bird C, Devanbu P (2012) Clones: what is that smell? Empir Softw Eng 17(4–5):503–530. https://doi.org/10.1007/s10664-011-9195-3
Saboury A, Musavi P, Khomh F, Antoniol G (2017) An empirical study of code smells in JavaScript projects. In: SANER 2017 - 24th IEEE international conference on software analysis, evolution, and reengineering, pp. 294–305. https://doi.org/10.1109/SANER.2017.7884630
Shatnawi R, Li W (2006) An investigation of bad smells in object-oriented design. In: Proceedings - third international conference oninformation technology: new generations. ITNG 2006, vol 2006, pp 161–163. https://doi.org/10.1109/ITNG.2006.31
Soltanifar B, Akbarinasaji S, Caglayan B, Bener AB, Filiz A, Kramer BM (2016) Software analytics in practice: a defect prediction model using code smells. In: Proceedings of the 20th international database engineering & applications symposium on - IDEAS ’16, pp 148–155. https://doi.org/10.1145/2938503.2938553
Taba SES, Khomh F, Zou Y, Hassan AE, Nagappan M (2013) Predicting bugs using antipatterns. In: IEEE international conference on software maintenance. ICSM, pp 270–279. https://doi.org/10.1109/ICSM.2013.38
Ubayawardana GM, Damith Karunaratna D (2019) Bug prediction model using code smells. In: 2018 18th international conference on advances in ICT for emerging regions (ICTer), pp 70–77. https://doi.org/10.1109/icter.2018.8615550
Yamashita A, Moonen L (2013) To what extent can maintenance problems be predicted by code smell detection? -an empirical study. Inf Softw Technol. https://doi.org/10.1016/j.infsof.2013.08.002
Zhang X, Zhou Y, Zhu C (2017) An empirical study of the impact of bad designs on defect proneness. In: Proceedings - 2017 annual conference on software analysis, testing and evolution, SATE 2017, vol 2017-Janua, pp 1–9. https://doi.org/10.1109/SATE.2017.9
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Piotrowski, P., Madeyski, L. (2020). Software Defect Prediction Using Bad Code Smells: A Systematic Literature Review. In: Poniszewska-Marańda, A., Kryvinska, N., Jarząbek, S., Madeyski, L. (eds) Data-Centric Business and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 40. Springer, Cham. https://doi.org/10.1007/978-3-030-34706-2_5
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