Abstract
There are several approaches to solving every coding algorithm in Computer Science. To achieve the same result, these methods may use various techniques and reasoning. The difficulty is that as the number of inputs increases, certain algorithms tend to perform poorly. Several metrics may be used to assess the quality of any code. The code runtime complexity is one of these measurements. To determine this runtime complexity, substantial study and a thorough understanding of algorithms are necessary, which is a challenging manual undertaking. In this study, the worst-case runtime complexity of codes in programming languages C, Java and Python are calculated as Big-O notations utilising code features like Abstract Syntax Trees, ML approaches and static code analysis. The novelty of the research is our labelled runtime complexity dataset which was constructed manually, implementing Deep Learning Algorithms like Bi-LSTM and calculating Code Runtime Complexity for the worst-case scenario as Big-O notations for codes in three languages, C, Java and Python. To predict the runtime complexity for a given code more accurately than the traditional legacy methods such as manually asserting code runtime complexity or running code for different amounts of input, we have presented a more effective manner for the same. The results portray that the XGBoost classifier outperforms the other models with an accuracy of 96%. The current study can also be extended to other high-level programming languages, including more training samples and making use of graph neural networks.
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References
Shunnarski A (2022) Welcome to the Big O Notation calculator! https://shunnarski.github.io/BigO.html. Accessed 05 May 2022
Sikka J, Satya K, Kumar Y, Uppal S, Shah RR, Zimmermann R (2020) Learning based methods for code runtime complexity prediction. Lect Notes Comput Sci 12035:313–325
Agenis-Nevers M, Bokde ND, Yaseen ZM, Shende MK (2020) An empirical estimation for time and memory algorithm complexities: newly developed R package. Multimedia Tools Appl 80(2):2997–3015
Hutter F, Xu L, Hoos HH, Leyton-Brown K (2014) Algorithm runtime prediction: methods & evaluation. Artif Intell 206:79–111
Haridas P, Chennupati G, Santhi N, Romero P, Eidenbenz S (2020) Code characterization with graph convolutions and capsule networks. IEEE Access 8:136307–136315. https://doi.org/10.1109/ACCESS.2020.3011909
Gao Y, Gu X, Zhang H, Lin H, Yang M (2021) Runtime performance prediction for deep learning models with graph neural network. MSR-TR-2021-3/Microsoft
Chen L, Ye W, Zhang S (2019) Capturing source code semantics via tree-based convolution over API-enhanced AST. In: Proceedings of the 16th ACM international conference on computing frontiers (n. Pag.)
Zhang J, Wang X, Zhang H, Sun H, Wang K, Liu X (2019) A novel neural source code representation based on abstract syntax tree. In: 2019 IEEE/ACM 41st international conference on software engineering (ICSE), pp 783–794. https://doi.org/10.1109/ICSE.2019.00086
Lin C, Ouyang Z, Zhuang J, Chen J, Li H, Wu R (2021) Improving code summarization with block-wise abstract syntax tree splitting. In: 2021 IEEE/ACM 29th international conference on program comprehension (ICPC), pp 184–195. https://doi.org/10.1109/ICPC52881.2021.00026
Kurniawati G, Karnalim O (2018) Introducing a practical educational tool for correlating algorithm time complexity with real program execution. J Inf Technol Comput Sci 3(1):1–15
Büch L, Andrzejak A (2019) Learning-based recursive aggregation of abstract syntax trees for code clone detection. In: 2019 IEEE 26th international conference on software analysis, evolution and reengineering (SANER), pp 95–104. https://doi.org/10.1109/SANER.2019.8668039
Wang W, Li, Bo Ma, Xin Xia, Zhi Jin. “Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree.” SANER 2020, London, ON, Canada 978–1–7281–5143–4/20/© 2020 IEEE.
Feng Q, Feng C, Hong W (2020) Graph neural network-based vulnerability predication. In: 2020 IEEE international conference on software maintenance and evolution (ICSME), pp 800–801. https://doi.org/10.1109/ICSME46990.2020.00096
Reza SM, Rahman Md, Parvez Md, Badreddin O, Al Mamun S (2020) Performance analysis of machine learning approaches in software complexity prediction. https://doi.org/10.1007/978-981-33-4673-4_3
Guzman J, Limoanco T (2017) An empirical approach to algorithm analysis resulting in approximations to big theta time complexity. J Softw 12:964–976. https://doi.org/10.17706/jsw.12.12.964-976
Ströder T, Aschermann C, Frohn F, Hensel J, Giesl J (2015) Aprove: termination and memory safety of C programs. In: Tools and algorithms for the construction and analysis of systems, pp 417–419
Rozemberczki B, Kiss O, Sarkar R (2020) Karate Club: An API oriented open-source python framework for unsupervised learning on graphs. In: Presented at proceedings of the 29th ACM international conference on information & knowledge management, Ireland
Narayanan A, Chandramohan M, Venkatesan R, Chen L, Liu Y, Jaiswal S (2017) graph2vec: learning distributed representations of graphs
Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. In: Proceedings of the 31st international conference on neural information processing systems (NIPS’17), Red Hook, NY, USA, pp 4768–4777
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357
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Deepa Shree, C.V., Kotian, J.D., Gupta, N., Adyapak, N.M., Ananthanagu, U. (2024). Predicting Code Runtime Complexity Using ML Techniques. In: Shetty, N.R., Prasad, N.H., Nalini, N. (eds) Advances in Computing and Information. ERCICA 2023. Lecture Notes in Electrical Engineering, vol 1104. Springer, Singapore. https://doi.org/10.1007/978-981-99-7622-5_26
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DOI: https://doi.org/10.1007/978-981-99-7622-5_26
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