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Jan Haltermann, Manuel Töws, Felix Pauck, Cedric Richter, Heike Wehrheim, Jürgen König, Arnab Sharma, Steffen Beringer, Oleksandra Koslova, Elisabeth Schlatt (left to right) Bildinformationen anzeigen

Jan Haltermann, Manuel Töws, Felix Pauck, Cedric Richter, Heike Wehrheim, Jürgen König, Arnab Sharma, Steffen Beringer, Oleksandra Koslova, Elisabeth Schlatt (left to right)

Arnab Sharma, M.Tech

Address: Arnab Sharma
Paderborn University
Faculty of Electrical Engineering, Computer Science and Mathematics
Warburger Str. 100
D-33098 Paderborn, Germany
Office:

O4.131

Phone: +49 (0) 5251-60-5388
Fax:  +49 (0) 5251-60-3993 

Email:

arnab.sharma(at)uni-paderborn.de
Secretary: Elisabeth Schlatt
Phone: +49 (0) 5251-60-3764
Email: schlatt(at)mail.upb.de
Office: O4.125

Publications


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Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets

S.B. Schneider, A. Sharma, H. Karl, H. Wehrheim, in: 2019 IFIP/IEEE International Symposium on Integrated Network Management (IM), IFIP, 2019, pp. 116--124

For optimal placement and orchestration of network services, it is crucial that their structure and semantics are specified clearly and comprehensively and are available to an orchestrator. Existing specification approaches are either ambiguous or miss important aspects regarding the behavior of virtual network functions (VNFs) forming a service. We propose to formally and unambiguously specify the behavior of these functions and services using Queuing Petri Nets (QPNs). QPNs are an established method that allows to express queuing, synchronization, stochastically distributed processing delays, and changing traffic volume and characteristics at each VNF. With QPNs, multiple VNFs can be connected to complete network services in any structure, even specifying bidirectional network services containing loops. We discuss how management and orchestration systems can benefit from our clear and comprehensive specification approach, leading to better placement of VNFs and improved Quality of Service. Another benefit of formally specifying network services with QPNs are diverse analysis options, which allow valuable insights such as the distribution of end-to-end delay. We propose a tool-based workflow that supports the specification of network services and the automatic generation of corresponding simulation code to enable an in-depth analysis of their behavior and performance.


Testing Balancedness of ML Algorithms

A. Sharma, H. Wehrheim. Testing Balancedness of ML Algorithms. 2019.


Testing Machine Learning Algorithms for Balanced Data Usage

A. Sharma, H. Wehrheim, in: IEEE International Conference on Software Testing, Verification and Validation (ICST), IEEE, 2019, pp. 125--135


Testing Balancedness of ML Algorithms

A. Sharma, H. Wehrheim, in: Software Engineering and Software Management, {SE/SWM} 2019, Stuttgart, Germany, February 18-22, 2019, {GI}, 2019, pp. 157-158

DOI


Automatic Fairness Testing of Machine Learning Models

A. Sharma, H. Wehrheim, in: Proceedings of the 32th IFIP International Conference on Testing Software and Systems (ICTSS), Springer, 2020


Testing Monotonicity of Machine Learning Models

A. Sharma, H. Wehrheim, CoRR (2020), abs/2002.12278


Higher Income, Larger Loan? Monotonicity Testing of Machine Learning Models

A. Sharma, H. Wehrheim, in: Proceedings of the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)., ACM, 2020


MLCheck- Property-Driven Testing of Machine Learning Models

A. Sharma, C. Demir, A. Ngonga Ngomo, H. Wehrheim, CoRR (2021), abs/2105.00741


MLCHECK–Property-Driven Testing of Machine Learning Classifiers

A. Sharma, C. Demir, A. Ngonga Ngomo, H. Wehrheim, in: Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, 2021

In recent years, we observe an increasing amount of software with machine learning components being deployed. This poses the question of quality assurance for such components: how can we validate whether specified requirements are fulfilled by a machine learned software? Current testing and verification approaches either focus on a single requirement (e.g., fairness) or specialize on a single type of machine learning model (e.g., neural networks). In this paper, we propose property-driven testing of machine learning models. Our approach MLCheck encompasses (1) a language for property specification, and (2) a technique for systematic test case generation. The specification language is comparable to property-based testing languages. Test case generation employs advanced verification technology for a systematic, property dependent construction of test suites, without additional user supplied generator functions. We evaluate MLCheck using requirements and data sets from three different application areas (software discrimination, learning on knowledge graphs and security). Our evaluation shows that despite its generality MLCheck can even outperform specialised testing approaches while having a comparable runtime


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Research interest

Testing of Machine Learning programs. 

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