تقرير
XtraLibD: Detecting Irrelevant Third-Party libraries in Java and Python Applications
| العنوان: | XtraLibD: Detecting Irrelevant Third-Party libraries in Java and Python Applications |
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| المؤلفون: | Kapur, Ritu, Rao, Poojith U, Dewan, Agrim, Sodhi, Balwinder |
| المصدر: | Springer's Communications in Computer and Information Science, vol 1556. Springer, Cham. Extended version of paper published in Evaluation of Novel Approaches to Software Engineering. ENASE 2021 |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Software Engineering |
| الوصف: | Software development comprises the use of multiple Third-Party Libraries (TPLs). However, the irrelevant libraries present in software application's distributable often lead to excessive consumption of resources such as CPU cycles, memory, and modile-devices' battery usage. Therefore, the identification and removal of unused TPLs present in an application are desirable. We present a rapid, storage-efficient, obfuscation-resilient method to detect the irrelevant-TPLs in Java and Python applications. Our approach's novel aspects are i) Computing a vector representation of a .class file using a model that we call Lib2Vec. The Lib2Vec model is trained using the Paragraph Vector Algorithm. ii) Before using it for training the Lib2Vec models, a .class file is converted to a normalized form via semantics-preserving transformations. iii) A eXtra Library Detector (XtraLibD) developed and tested with 27 different language-specific Lib2Vec models. These models were trained using different parameters and >30,000 .class and >478,000 .py files taken from >100 different Java libraries and 43,711 Python available at MavenCentral.com and Pypi.com, respectively. XtraLibD achieves an accuracy of 99.48% with an F1 score of 0.968 and outperforms the existing tools, viz., LibScout, LiteRadar, and LibD with an accuracy improvement of 74.5%, 30.33%, and 14.1%, respectively. Compared with LibD, XtraLibD achieves a response time improvement of 61.37% and a storage reduction of 87.93% (99.85% over JIngredient). Our program artifacts are available at https://www.doi.org/10.5281/zenodo.5179747. Comment: 25 pages, 5 figures, 4 tables, Book Chapter of Springer's Communications in Computer and Information Science, vol 1556. Springer, Cham. Extended version of paper published in Evaluation of Novel Approaches to Software Engineering. ENASE 2021 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/978-3-030-96648-5_7 |
| URL الوصول: | http://arxiv.org/abs/2202.10776 |
| رقم الأكسشن: | edsarx.2202.10776 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/978-3-030-96648-5_7 |
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