[2208.08603] An Empirical Evaluation of Competitive Programming AI: A Case Study of AlphaCode
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Computer Science > Software Engineering

arXiv:2208.08603 (cs)
[Submitted on 18 Aug 2022 (v1), last revised 26 Aug 2022 (this version, v2)]

Title:An Empirical Evaluation of Competitive Programming AI: A Case Study of AlphaCode

Authors:Sila Lertbanjongngam, Bodin Chinthanet, Takashi Ishio, Raula Gaikovina Kula, Pattara Leelaprute, Bundit Manaskasemsak, Arnon Rungsawang, Kenichi Matsumoto
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Abstract:AlphaCode is a code generation system for assisting software developers in solving competitive programming problems using natural language problem descriptions. Despite the advantages of the code generating system, the open source community expressed concerns about practicality and data licensing. However, there is no research investigating generated codes in terms of code clone and performance. In this paper, we conduct an empirical study to find code similarities and performance differences between AlphaCode-generated codes and human codes. The results show that (i) the generated codes from AlphaCode are similar to human codes (i.e., the average maximum similarity score is 0.56) and (ii) the generated code performs on par with or worse than the human code in terms of execution time and memory usage. Moreover, AlphaCode tends to generate more similar codes to humans for low-difficulty problems (i.e., four cases have the exact same codes). It also employs excessive nested loops and unnecessary variable declarations for high-difficulty problems, which cause low performance regarding our manual investigation. The replication package is available at https:/doi.org/10.5281/zenodo.6820681
Comments: Accepted to the 16th International Workshop on Software Clones (IWSC 2022)
Subjects: Software Engineering (cs.SE)
Cite as: arXiv:2208.08603 [cs.SE]
  (or arXiv:2208.08603v2 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2208.08603

Submission history

From: Bodin Chinthanet [view email]
[v1] Thu, 18 Aug 2022 02:48:35 UTC (1,739 KB)
[v2] Fri, 26 Aug 2022 03:53:48 UTC (1,739 KB)
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