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pro vyhledávání: '"Sinha, Saurabh."'
Implementing automated unit tests is an important but time consuming activity in software development. Developers dedicate substantial time to writing tests for validating an application and preventing regressions. To support developers in this task,
Externí odkaz:
http://arxiv.org/abs/2409.03093
The widespread adoption of REST APIs, coupled with their growing complexity and size, has led to the need for automated REST API testing tools. Current tools focus on the structured data in REST API specifications but often neglect valuable insights
Externí odkaz:
http://arxiv.org/abs/2312.00894
Modern web services increasingly rely on REST APIs. Effectively testing these APIs is challenging due to the vast search space to be explored, which involves selecting API operations for sequence creation, choosing parameters for each operation from
Externí odkaz:
http://arxiv.org/abs/2309.04583
Autor:
Pan, Rangeet, Ibrahimzada, Ali Reza, Krishna, Rahul, Sankar, Divya, Wassi, Lambert Pouguem, Merler, Michele, Sobolev, Boris, Pavuluri, Raju, Sinha, Saurabh, Jabbarvand, Reyhaneh
Code translation aims to convert source code from one programming language (PL) to another. Given the promising abilities of large language models (LLMs) in code synthesis, researchers are exploring their potential to automate code translation. The p
Externí odkaz:
http://arxiv.org/abs/2308.03109
Modern web applications make extensive use of API calls to update the UI state in response to user events or server-side changes. For such applications, API-level testing can play an important role, in-between unit-level testing and UI-level (or end-
Externí odkaz:
http://arxiv.org/abs/2305.14692
Autor:
Ghaffari, Saba, Saleh, Ehsan, Schwing, Alexander G., Wang, Yu-Xiong, Burke, Martin D., Sinha, Saurabh
Protein design, a grand challenge of the day, involves optimization on a fitness landscape, and leading methods adopt a model-based approach where a model is trained on a training set (protein sequences and fitness) and proposes candidates to explore
Externí odkaz:
http://arxiv.org/abs/2305.13650
Autor:
Dibaeinia, Payam, Sinha, Saurabh
The discovery of causal relationships from high-dimensional data is a major open problem in bioinformatics. Machine learning and feature attribution models have shown great promise in this context but lack causal interpretation. Here, we show that a
Externí odkaz:
http://arxiv.org/abs/2304.12523
Publikováno v:
InProceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis 2022 Jul 18 (pp. 289-301)
Modern web services routinely provide REST APIs for clients to access their functionality. These APIs present unique challenges and opportunities for automated testing, driving the recent development of many techniques and tools that generate test ca
Externí odkaz:
http://arxiv.org/abs/2204.08348
Publikováno v:
In Prostaglandins and Other Lipid Mediators October 2024 174