Performance Shell Benchmark Correctness, Efficiency, and Beyond

Research Article
Open access

Performance Shell Benchmark Correctness, Efficiency, and Beyond

Yizhang Xu 1*
  • 1 Beijing University of Post and Telecommunication, Beijing, China    
  • *corresponding author xuyizhang@bupt.edu.cn
Published on 8 February 2025 | https://doi.org/10.54254/2755-2721/2024.20797
ACE Vol.132
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-941-0
ISBN (Online): 978-1-83558-942-7

Abstract

Shell script is pivotal in tackling diverse real-world issues such as COVID-19 analytics, NLP (Natural Language Processing), and data analysis. Yet, they can be prone to failure or inefficiency. This paper rigorously assesses the reliability and speed of these benchmark shell scripts. For verifying output integrity, this study leverage SHA-256 hashes and the diff utility for swift and accurate comparisons with the correct outputs. To measure performance, the time command is employed to capture and log execution times. Ultimately, our analysis reveals that 82/204 of the shell script outputs are accurate, demonstrating robust performance even under the demands of large-scale data processing.

Keywords:

Shell Script, PaSh, Reliability and Speed, Performance Measurement, SHA-256 hashes

Xu,Y. (2025). Performance Shell Benchmark Correctness, Efficiency, and Beyond. Applied and Computational Engineering,132,250-256.
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References

[1]. Chris Johnson. Shell Scripting Recipes: A Problem-solution Approach. Apress, 2006.

[2]. Cameron Newham. Learning the bash shell: Unix shell programming. ” O’Reilly Media, Inc.”, 2005.

[3]. Ganesh Sanjiv Naik. Learning Linux Shell Scripting: Leverage the power of shell scripts to solve real-world problems. Packt Publishing Ltd, 2018.

[4]. Leonardo Leite, Carlos Eduardo Moreira, Daniel Cordeiro, Marco Aurelio Gerosa, and Fabio Kon. Deploying large-scale´ service compositions on the cloud with the choreos enactment engine. In 2014 IEEE 13th international symposium on network computing and applications, pages 121–128. IEEE, 2014.

[5]. Andre Goforth. The role and impact of software coding standards on system integrity. In AIAA Infotech@ Aerospace (I@ A) Conference, page 5222, 2013.

[6]. Deepti Raghavan, Sadjad Fouladi, Philip Levis, and Matei Zaharia. {POSH}: A {Data-Aware} shell. In 2020 USENIX Annual Technical Conference (USENIX ATC 20), pages 617–631, 2020.

[7]. Nikos Vasilakis and Konstantinos Kallas. Pash: Light-touch data-parallel shell processing. In ProceedingsoftheSixteenth European Conference on Computer Systems, pages 49–66, 2021.

[8]. Tammam Mustafa, Konstantinos Kallas, Pratyush Das, and Nikos Vasilakis. {DiSh}: Dynamic {Shell-Script} distribution. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23), pages 341– 356, 2023.

[9]. Dan Ofer, Nadav Brandes, and Michal Linial. The language of proteins: Nlp, machine learning & protein sequences. Computational and Structural Biotechnology Journal, 19:1750–1758, 2021.

[10]. Dimitris Karnikis and Tammam Mustafa. Binpash benchmark.

[11]. Tammam Mustafa. Parallel and Distributed Just-in-Time Shell Script Compilation. PhD thesis, Massachusetts Institute of Technology, 2022.

[12]. Konstantinos Kallas, Filip Niksic, Caleb Stanford, and Rajeev Alur. Diffstream: differential output testing for stream processing programs. Proceedings of the ACM on Programming Languages, 4(OOPSLA):1–29, 2020.

[13]. Jinsuk Kim, Dong-Hoon Yoo, Heejin Jang, and Kimoon Jeong. Webshark 1.0: a benchmark collection for malicious web shell detection. Journal of Information Processing Systems, 11(2):229–238, 2015.

[14]. Gong Fan. NVIDIA Performance Testing for Emulation of the Grace CPU. PhD thesis, NVIDIA Corporation, 2021.

[15]. Olga Manankova, Mubarak Yakubova, and Alimjan Baikenov. Cryptanalysis the sha-256 hash function using rainbow tables. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 10(4):930–944, 2022.

Cite this article

Xu,Y. (2025). Performance Shell Benchmark Correctness, Efficiency, and Beyond. Applied and Computational Engineering,132,250-256.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation

ISBN：978-1-83558-941-0(Print) / 978-1-83558-942-7(Online)
Editor：Mustafa ISTANBULLU
Conference website: https://2024.confmla.org/
Conference date: 21 November 2024
Series: Applied and Computational Engineering
Volume number: Vol.132
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
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References

[1]. Chris Johnson. Shell Scripting Recipes: A Problem-solution Approach. Apress, 2006.

[2]. Cameron Newham. Learning the bash shell: Unix shell programming. ” O’Reilly Media, Inc.”, 2005.

[3]. Ganesh Sanjiv Naik. Learning Linux Shell Scripting: Leverage the power of shell scripts to solve real-world problems. Packt Publishing Ltd, 2018.

[4]. Leonardo Leite, Carlos Eduardo Moreira, Daniel Cordeiro, Marco Aurelio Gerosa, and Fabio Kon. Deploying large-scale´ service compositions on the cloud with the choreos enactment engine. In 2014 IEEE 13th international symposium on network computing and applications, pages 121–128. IEEE, 2014.

[5]. Andre Goforth. The role and impact of software coding standards on system integrity. In AIAA Infotech@ Aerospace (I@ A) Conference, page 5222, 2013.

[6]. Deepti Raghavan, Sadjad Fouladi, Philip Levis, and Matei Zaharia. {POSH}: A {Data-Aware} shell. In 2020 USENIX Annual Technical Conference (USENIX ATC 20), pages 617–631, 2020.

[7]. Nikos Vasilakis and Konstantinos Kallas. Pash: Light-touch data-parallel shell processing. In ProceedingsoftheSixteenth European Conference on Computer Systems, pages 49–66, 2021.

[8]. Tammam Mustafa, Konstantinos Kallas, Pratyush Das, and Nikos Vasilakis. {DiSh}: Dynamic {Shell-Script} distribution. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23), pages 341– 356, 2023.

[9]. Dan Ofer, Nadav Brandes, and Michal Linial. The language of proteins: Nlp, machine learning & protein sequences. Computational and Structural Biotechnology Journal, 19:1750–1758, 2021.

[10]. Dimitris Karnikis and Tammam Mustafa. Binpash benchmark.

[11]. Tammam Mustafa. Parallel and Distributed Just-in-Time Shell Script Compilation. PhD thesis, Massachusetts Institute of Technology, 2022.

[12]. Konstantinos Kallas, Filip Niksic, Caleb Stanford, and Rajeev Alur. Diffstream: differential output testing for stream processing programs. Proceedings of the ACM on Programming Languages, 4(OOPSLA):1–29, 2020.

[13]. Jinsuk Kim, Dong-Hoon Yoo, Heejin Jang, and Kimoon Jeong. Webshark 1.0: a benchmark collection for malicious web shell detection. Journal of Information Processing Systems, 11(2):229–238, 2015.

[14]. Gong Fan. NVIDIA Performance Testing for Emulation of the Grace CPU. PhD thesis, NVIDIA Corporation, 2021.

[15]. Olga Manankova, Mubarak Yakubova, and Alimjan Baikenov. Cryptanalysis the sha-256 hash function using rainbow tables. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 10(4):930–944, 2022.

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