SavedAdd your journal

Journals / Düzce Üniversitesi Bilim ve Teknoloji Dergisi / 2022 / Cilt: 10 - Sayı: 1

Discovering Sequential Source Code Patterns in Software Engineering

Yazılım Mühendisliğinde Sıralı Kaynak Kodu Modellerini Keşfetme

Kökten Ulaş BİRANT
Dilara KIRNAPCI
Pages
309–324
Publication Date
2022-10-12
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The experiments that carried out on an open-source software project showed that the proposed SCodeMiner framework is an effective mining tool in identifying coding patterns.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Discovering sequential patterns in source codes is an important issue in software engineering since it can provide useful knowledge to help in a variety of tasks such as code completion, code refactoring, developer profiling, and code complexity measurement.
📊
Key Findings & Data:This paper proposes a new framework, called Source Code Miner (SCodeMiner), which discovers frequent sequential rules within a software project.
💡
Conclusion & Impact:The experiments that carried out on an open-source software project showed that the proposed SCodeMiner framework is an effective mining tool in identifying coding patterns.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Kaynak kod örüntüleri · Sıralı örüntü madenciliği · Yazılım mühendisliği

Abstract

Discovering sequential patterns in source codes is an important issue in software engineering since it can provide useful knowledge to help in a variety of tasks such as code completion, code refactoring, developer profiling, and code complexity measurement. This paper proposes a new framework, called Source Code Miner (SCodeMiner), which discovers frequent sequential rules within a software project. The proposed framework firstly transforms a Java code into a sequence data and then applies a sequential pattern mining (SPM) algorithm. This study is also original in that it compares four SPM algorithms in terms of computational time, including sequential pattern discovery using equivalence classes (SPADE), prefix-projected sequential pattern mining (PrefixSpan), bi-directional extension (BIDE+), and last position induction (LAPIN). The experiments that carried out on an open-source software project showed that the proposed SCodeMiner framework is an effective mining tool in identifying coding patterns.

Özet

Kaynak kodlardaki sıralı örüntüleri keşfetmek yazılım mühendisliğinde önemli bir konudur, çünkü kod tamamlama, kodu yeniden düzenleme, geliştirici profili oluşturma, ve kod karmaşıklığı ölçümü gibi çeşitli işlemlerde yardımcı olacak yararlı bilgiler sağlayabilmektedir. Bu makale, bir yazılım projesinde sıkça geçen sıralı kuralları keşfeden ve Kaynak Kod Madencisi (SCodeMiner) adı verilen yeni bir yazılım çerçevesi önermektedir. Önerilen yazılım çerçevesi ilk olarak bir Java kaynak kodunu bir sıralı veri tabanına dönüştürür ve ardından bir sıralı örüntü madenciliği (SPM) algoritması uygular. Bu çalışma aynı zamanda, dört SPM algoritmasını çalışma süresi açısından karşılaştırması açısından da orijinaldir. Bu algoritmalar şunlardır: ön ek ile öngörülen sıralı örüntü madenciliği (PrefixSpan), denklik sınıflarını kullanarak sıralı örüntü keşfi (SPADE), çift yönlü uzatma (BIDE+), ve son pozisyon indüksiyonu (LAPIN). Açık kaynak kodlu bir yazılım projesi üzerinde gerçekleştirilen deneyler, önerilen SCodeMiner yazılım çerçevesinin kodlama örüntülerini belirlemede etkili bir madencilik aracı olduğunu göstermektedir.

🔖

Cite This Article

BİRANT, K. U., KIRNAPCI, D. (2022). Discovering Sequential Source Code Patterns in Software Engineering. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, Cilt: 10 - Sayı: 1, 309–324. https://ajindex.com/articles/493821

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. A. Agrawal, M. Alenezi, R. Kumar, and R. A. Khan, “Securing web applications through a framework of source code analysis,” J. Comput. Sci., vol. 15, no. 12, pp. 1780-1794, 2019
  2. F. Ebert, F. Castor, N. Novielli, and A. Serebrenik, “An exploratory study on confusion in code reviews,” Empirical Software Eng., vol. 26, no. 12, pp. 1-48, 2021
  3. S. Proksch, J. Lerch, and M. Mezini, “Intelligent code completion with Bayesian networks,” ACM Trans. Softw. Eng. Methodol., vol. 25, no. 1, pp. 1-31, 2015
  4. M. M. Rahman, Y. Watanobe, K. Nakamura, and M. Bures, “A neural network based intelligent support model for program code completion,” Sci. Program., vol. 2020, pp. 1-18, 2020
  5. L. Kaur and A. Mishra, “Cognitive complexity as a quantifier of version to version Java-based source code change: An empirical probe,” Inf. Softw. Technol, vol. 106, pp. 31-48, 2019
  6. A. A. Abdelaal, S. Abed, M. Al-Shayeji, and M. Allaho, “Customized frequent patterns mining algorithms for enhanced Top-Rank-K frequent pattern mining,” Expert Syst. Appl., vol. 169, pp. 1-14, 2021
  7. W. Gan, J. C.-W. Lin, P. Fournier-Viger, H.-C. Chao, and P. S. Yu, “A survey of parallel sequential pattern mining,” ACM Trans. Knowl. Discovery Data, vol. 13, no. 3, pp. 1-34, 2019
  8. J. Pei et al., “Mining sequential patterns by pattern-growth: The PrefixSpan approach,” IEEE Trans. Knowl. Data Eng., vol. 16, no. 10, pp. 1-17, 2004
  9. M. J. Zaki, “SPADE: An efficient algorithm for mining frequent sequences,” Mach. Learn., vol. 42, pp. 31-60, 2001
  10. J. Wang and J. Han, “BIDE: Efficient mining of frequent closed sequences,” in Proc. 20th Int. Conf. on Data Eng., Boston, MA, USA, 2004, pp. 79-90
  11. Z. Yang, Y. Wang, and M. Kitsuregawa, “LAPIN: Effective sequential pattern mining algorithms by last position induction for dense databases,” in 12th Int. Conf. on Database Syst. for Adv. Appl., Bangkok, Thailand, 2007, pp. 1020-1023
  12. S. Cao, X. Sun, L. Bo, Y. Wei, and B. Li, "BGNN4VD: Constructing bidirectional graph neural-network for vulnerability detection," Inf. Softw. Technol., vol. 136, pp. 1-11, 2021
  13. S. Jeon and H. K. Kim, "AutoVAS: An automated vulnerability analysis system with a deep learning approach," Comput. Secur., vol 106, pp. 1-24, 2021
  14. C. D. Newman et al., “On the generation, structure, and semantics of grammar patterns in source code identifiers,” J. Syst. Softw., vol. 170, pp. 1-21, 2020
  15. X. Li, L. Wang, Y. Xin, Y. Yang, and Y. Chen, "Automated vul