BloatLibD: Detecting Irrelevant Libraries in Java Applications

Dewan, Agrim; Rao, Poojith U.; Sodhi, Balwinder; Kapur, Ritu

doi:10.5220/0010459401260137

Cited by 6 publications

(7 citation statements)

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“…The details about these tools have been discussed in Section 2. We already conducted the experiments with LiteRadar, LibD, and LibScout in our previous research work [10], and extend our previous work by providing the TPL-detection support for python-based TPLs. In this section, we provide the details of new experiments perfomed with some of the recent Java-based and Python-based TPL detection tools, viz., DepClean, JIngredient, Autoflake, PyCln, and also a summarized comparison of performance results obtained in our previous comparisons [10] with LiteRadar, LibD, and LibScout.…”

Section: Performance Comparison Of Xtralibd With the Existing Tpl-det...mentioning

confidence: 72%

“…We already conducted the experiments with LiteRadar, LibD, and LibScout in our previous research work [10], and extend our previous work by providing the TPL-detection support for python-based TPLs. In this section, we provide the details of new experiments perfomed with some of the recent Java-based and Python-based TPL detection tools, viz., DepClean, JIngredient, Autoflake, PyCln, and also a summarized comparison of performance results obtained in our previous comparisons [10] with LiteRadar, LibD, and LibScout. Though PyCln and Autoflake work in detection of used/ unused import TPLs, and not the detection of TPLs, we included them as in our research, we found them to be the closest available python tools working with TPLs.…”

Section: Performance Comparison Of Xtralibd With the Existing Tpl-det...mentioning

confidence: 72%

“…To obtain this set of "available TPLs," we leverage the TPLs present at MavenCentral repository 4 that hosts TPLs for various functionalities, which we assume to be a trustworthy source. In our previous work [10], we also validated the efficacy of PVA in computing a reliable and accurate representation of binary and textual code. The current work aims to extend the existing method for Python applications.…”

Section: Motivationmentioning

confidence: 78%

“…The idea can be leveraged to develop various software artifacts for automating the SDLC activities, such as software code review, source code recommendation, and code clone detection. Table 4: Scenarios for training Lib2Vec models using PVA [10].…”

Section: Discussionmentioning

confidence: 99%

“…In our recent work, we proposed an obfuscation-resilient tool, Bloat Library Detector (BloatLibD) [10] 3 , that detects the TPLs present in a given Java application by identifying the similarities between the source code of the "available TPLs" and the TPLs used in the given application. To obtain this set of "available TPLs," we leverage the TPLs present at MavenCentral repository 4 that hosts TPLs for various functionalities, which we assume to be a trustworthy source.…”

Section: Motivationmentioning

confidence: 99%

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

Kapur,

Rao,

Dewan

et al. 2022

Preprint

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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.

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Section: Performance Comparison Of Xtralibd With the Existing Tpl-det...mentioning

confidence: 72%

Section: Performance Comparison Of Xtralibd With the Existing Tpl-det...mentioning

confidence: 72%

Section: Motivationmentioning

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

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