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Software development comprises the use of multiple Third-Party Libraries(TPLs). However, the irrelevant libraries present in software application'sdistributable often lead to excessive consumption of resources such as CPUcycles, memory, and modile-devices' battery usage. Therefore, theidentification and removal of unused TPLs present in an application aredesirable. We present a rapid, storage-efficient, obfuscation-resilient methodto detect the irrelevant-TPLs in Java and Python applications. Our approach'snovel aspects are i) Computing a vector representation of a .class file using amodel that we call Lib2Vec. The Lib2Vec model is trained using the ParagraphVector Algorithm. ii) Before using it for training the Lib2Vec models, a .classfile is converted to a normalized form via semantics-preservingtransformations. iii) A eXtra Library Detector (XtraLibD) developed and testedwith 27 different language-specific Lib2Vec models. These models were trainedusing different parameters and >30,000 .class and >478,000 .py files taken from>100 different Java libraries and 43,711 Python available at MavenCentral.comand Pypi.com, respectively. XtraLibD achieves an accuracy of 99.48% with an F1score 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 improvementof 61.37% and a storage reduction of 87.93% (99.85% over JIngredient). Ourprogram artifacts are available at https://www.doi.org/10.5281/zenodo.5179747.

XtraLibD: Detecting Irrelevant Third-Party libraries in Java and Python Applications