Aditya Kanade

844 total citations · 1 hit paper
12 papers, 454 citations indexed

About

Aditya Kanade is a scholar working on Software, Information Systems and Signal Processing. According to data from OpenAlex, Aditya Kanade has authored 12 papers receiving a total of 454 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Software, 6 papers in Information Systems and 4 papers in Signal Processing. Recurrent topics in Aditya Kanade's work include Software Engineering Research (6 papers), Software Testing and Debugging Techniques (5 papers) and Advanced Malware Detection Techniques (4 papers). Aditya Kanade is often cited by papers focused on Software Engineering Research (6 papers), Software Testing and Debugging Techniques (5 papers) and Advanced Malware Detection Techniques (4 papers). Aditya Kanade collaborates with scholars based in India, Germany and United States. Aditya Kanade's co-authors include Shirish Shevade, Rahul Gupta, Gogul Balakrishnan, Petros Maniatis, Kensen Shi, Rupak Majumdar, M. Manivannan, Saikat Chakraborty, Ashish Tiwari and Arjun Radhakrishna and has published in prestigious journals such as ACM SIGPLAN Notices, ACM Transactions on Software Engineering and Methodology and Software Practice and Experience.

In The Last Decade

Aditya Kanade

12 papers receiving 449 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

DeepFix: Fixing Common C Language Errors by Deep Learning

2017 233 citations Rahul Gupta, Aditya Kanade et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers

Aditya Kanade

IS

SOFTW

AI

SP

CNC

Hila Peleg Israel

Rohan Padhye United States

Zhongxin Liu China

Maria Christakis Germany

Valerio Terragni New Zealand

Robert W. Bowdidge United States

Yoonki Song United States

Yoann Padioleau Denmark

Tianxiao Gu China

André Takeshi Endo Brazil

Hila Peleg Israel

Aditya Kanade

192 ×0.6

IS

221 ×0.8

SOFTW

115 ×0.9

AI

111 ×0.9

SP

46 ×0.5

CNC

Citations per year, relative to Aditya Kanade Aditya Kanade (= 1×) peers Hila Peleg

Countries citing papers authored by Aditya Kanade

Since Specialization

Citations

This map shows the geographic impact of Aditya Kanade's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Aditya Kanade with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aditya Kanade more than expected).

Fields of papers citing papers by Aditya Kanade

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aditya Kanade. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Aditya Kanade. The network helps show where Aditya Kanade may publish in the future.

Co-authorship network of co-authors of Aditya Kanade

This figure shows the co-authorship network connecting the top 25 collaborators of Aditya Kanade. A scholar is included among the top collaborators of Aditya Kanade based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Aditya Kanade. Aditya Kanade is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

12 of 12 papers shown

1.

Chakraborty, Saikat, Sumit Gulwani, Aditya Kanade, et al.. (2023). Grace: Language Models Meet Code Edits. 1483–1495. 6 indexed citations

2.

Kanade, Aditya, et al.. (2023). Attention-Guided Deep Learning Framework For Movement Quality Assessment. 1–5. 5 indexed citations

3.

Kanade, Aditya, et al.. (2022). Towards Multidimensional Textural Perception and Classification Through Whisker. 1–7. 2 indexed citations

4.

Kanade, Aditya, Petros Maniatis, Gogul Balakrishnan, & Kensen Shi. (2019). Pre-trained Contextual Embedding of Source Code. arXiv (Cornell University). 24 indexed citations

5.

Kanade, Aditya, Petros Maniatis, Gogul Balakrishnan, & Kensen Shi. (2019). Learning and Evaluating Contextual Embedding of Source Code. arXiv (Cornell University). 1. 5110–5121. 92 indexed citations

6.

Gupta, Rahul, Aditya Kanade, & Shirish Shevade. (2019). Deep Reinforcement Learning for Syntactic Error Repair in Student Programs. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 930–937. 33 indexed citations

7.

Gupta, Rahul, et al.. (2017). DeepFix: Fixing Common C Language Errors by Deep Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 31(1). 233 indexed citations breakdown →

8.

Kanade, Aditya, et al.. (2016). Asynchrony-aware static analysis of Android applications. 163–172. 3 indexed citations

9.

Kanade, Aditya, et al.. (2015). Rely/Guarantee Reasoning for Asynchronous Programs. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 6 indexed citations

10.

Kanade, Aditya, et al.. (2014). Mining Unit Tests for Discovery and Migration of Math APIs. ACM Transactions on Software Engineering and Methodology. 24(1). 1–33. 5 indexed citations

11.

Kanade, Aditya, et al.. (2014). Race detection for Android applications. ACM SIGPLAN Notices. 49(6). 316–325. 44 indexed citations

12.

Kanade, Aditya, et al.. (2009). Validation of GCC optimizers through trace generation. Software Practice and Experience. 39(6). 611–639. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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