Umar Farooq

2025

1. QSW’25

   Analyzing the Evolution and Maintenance of Quantum Software Repositories Best Paper Award

   Krishna Upadhyay, Vinaik Chhetri, AB Siddique, and Umar Farooq

   In Proceedings of the 2025 IEEE International Conference on Quantum Software, 2025

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   Quantum computing is rapidly advancing, but quantum software development faces significant challenges, including a steep learning curve, high hardware error rates, and a lack of mature engineering practices. This study conducts a large-scale mining analysis of over 21,000 GitHub repositories, containing 1.2 million commits from more than 10,000 developers, to examine the evolution and maintenance of quantum software. We analyze repository growth, programming language and framework adoption, and contributor trends, revealing a 200% increase in repositories and a 150% rise in contributors since 2017. Additionally, we investigate software development and maintenance practices, showing that perfective commits dominate (51.76%), while the low occurrence of corrective commits (18.54%) indicates potential gaps in bug resolution. Furthermore, 34% of reported issues are quantum-specific, highlighting the need for specialized debugging tools beyond conventional software engineering approaches. This study provides empirical insights into the software engineering challenges of quantum computing, offering recommendations to improve development workflows, tooling, and documentation. We are also open-sourcing our dataset to support further analysis by the community and to guide future research and tool development for quantum computing. The dataset is available at: https://github.com/kriss-u/QRepoAnalysis-Paper

   @inproceedings{qsw2025,
     author = {Upadhyay, Krishna and Chhetri, Vinaik and Siddique, AB and Farooq, Umar},
     booktitle = {Proceedings of the 2025 IEEE International Conference on Quantum Software},
     title = {Analyzing the Evolution and Maintenance of Quantum Software Repositories},
     year = {2025},
     award = {Best Paper Award},
     doi = {10.48550/arXiv.2501.06894},
     url = {https://doi.org/10.48550/arXiv.2501.06894},
     publisher = {IEEE Computer Society},
     address = {Los Alamitos, CA, USA}
   }

2. ICS’25

   GraCFL: A Holistically Designed Vertex-Centric Graph System for CFL Reachability

   Sakib Fuad, Amir Hossein Nodehi Sabet, Umar Farooq, and Zhijia Zhao

   In Proceedings of the 2025 International Conference on Supercomputing, 2025

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   Many program analyses can be formulated as context-free language (CFL) reachability problems on an edge-labeled graph. While graph systems have been proposed recently for large-scale CFL reachability analysis of system software, the design space has not yet been systematically explored, leading to sub-optimal performance. This workpresents GraCFL, a holistically designed graph system for CFL reachability. Inspired by the vertex-centric processing paradigm, we formalize CFL reachability using a multi-directional vertex-centric model. We then analyze this model in terms of computation redundancy, strategies for deriving new reachability, data locality, and parallelism. The analysis reveals a set of insights that guide the design of new techniques and optimizations to improve system performance. As a result of the systematic design, GraCFL demonstrates superior performance compared to state-ofthe-art graph systems, with an average 14.14× speedup over Graspan and 8.33× speedup over POCR. Its source code is available at https://github.com/AutomataLab/GraCFL.

   @inproceedings{ics2025,
     author = {Fuad, Sakib and Sabet, Amir Hossein Nodehi and Farooq, Umar and Zhao, Zhijia},
     title = {GraCFL: A Holistically Designed Vertex-Centric Graph System for CFL Reachability},
     year = {2025},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     booktitle = {Proceedings of the 2025 International Conference on Supercomputing},
     numpages = {15},
     location = {Salt Lake City, UT, USA},
     series = {ICS '25},
     doi = {10.1145/3721145.3725762}
   }

3. S&P’25

   Assessing and Enhancing Quantum Readiness in Mobile Apps

   Joseph Strauss, Krishna Upadhyay, AB Siddique, Ibrahim Baggili, and Umar Farooq

   In 46th IEEE Symposium on Security and Privacy, 2025

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   Quantum computers threaten widely deployed cryptographic primitives such as RSA, DSA, and ECC. While NIST has released post-quantum cryptographic (PQC) standards (e.g., Kyber, Dilithium), mobile app ecosystems remain largely unprepared for this transition. We present a large-scale binary analysis of over 4,000 Android apps to assess cryptographic readiness. Our results show widespread reliance on quantum-vulnerable algorithms such as MD5, SHA-1, and RSA, while PQC adoption remains absent in production apps. To bridge the readiness gap, we explore LLM-assisted migration. We evaluate leading LLMs (GPT-4o, Gemini Flash, Claude Sonnet, etc.) for automated cryptographic migration. All models successfully performed simple hash replacements (e.g., SHA-1 to SHA-256). However, none produced correct PQC upgrades due to multi-file changes, missing imports, and lack of context awareness. These results underscore the need for structured guidance and system-aware tooling for post-quantum migration.

   @inproceedings{sp2025,
     author = {Strauss, Joseph and Upadhyay, Krishna and Siddique, AB and Baggili, Ibrahim and Farooq, Umar},
     booktitle = {46th IEEE Symposium on Security and Privacy},
     title = {Assessing and Enhancing Quantum Readiness in Mobile Apps},
     year = {2025},
     volume = {},
     issn = {},
     doi = {10.48550/arXiv.2506.00790},
     url = {https://doi.org/10.48550/arXiv.2506.00790}
   }

2023

1. SIGIR’23

   MobileRec: A Large Scale Dataset for Mobile Apps Recommendation

   MH Maqbool, Umar Farooq, Adib Mosharrof, AB Siddique, and Hassan Foroosh

   In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

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   Recommender systems have become ubiquitous in our digital lives, from recommending products on e-commerce websites to suggesting movies and music on streaming platforms. Existing recommendation datasets, such as Amazon Product Reviews and MovieLens, greatly facilitated the research and development of recommender systems in their respective domains. While the number of mobile users and applications (aka apps) has increased exponentially over the past decade, research in mobile app recommender systems has been significantly constrained, primarily due to the lack of high-quality benchmark datasets, as opposed to recommendations for products, movies, and news. To facilitate research for app recommendation systems, we introduce a large-scale dataset, called MobileRec. We constructed MobileRec from users’ activity on the Google play store. MobileRec contains 19.3 million user interactions (i.e., user reviews on apps) with over 10K unique apps across 48 categories. MobileRec records the sequential activity of a total of 0.7 million distinct users. Each of these users has interacted with no fewer than five distinct apps, which stands in contrast to previous datasets on mobile apps that recorded only a single interaction per user. Furthermore, MobileRec presents users’ ratings as well as sentiments on installed apps, and each app contains rich metadata such as app name, category, description, and overall rating, among others. We demonstrate that MobileRec can serve as an excellent testbed for app recommendation through a comparative study of several state-of-the-art recommendation approaches. The quantitative results can act as a baseline for other researchers to compare their results against. The MobileRec dataset is available at https://huggingface.co/datasets/recmeapp/mobilerec.

   @inproceedings{sigir2023,
     author = {Maqbool, MH and Farooq, Umar and Mosharrof, Adib and Siddique, AB and Foroosh, Hassan},
     title = {MobileRec: A Large Scale Dataset for Mobile Apps Recommendation},
     year = {2023},
     isbn = {9781450394086},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     booktitle = {Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
     pages = {3007–3016},
     numpages = {10},
     keywords = {googleplay dataset, recommendation, app recommendation},
     location = {Taipei, Taiwan},
     series = {SIGIR '23},
     doi = {10.1145/3539618.3591906}
   }

2022

1. BigData’22

   Proactive Prioritization of App Issues via Contrastive Learning

   Moghis Fereidouni, Adib Mosharrof, Umar Farooq, and A.B. Siddique

   In Proceedings of the 2022 IEEE International Conference on Big Data (Big Data), Dec 2022

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   Mobile app stores produce a tremendous amount of data in the form of user reviews, which is a huge source of user requirements and sentiments; such reviews allow app developers to proactively address issues in their apps. However, only a small number of reviews capture common issues and sentiments which creates a need for automatically identifying prominent reviews. Unfortunately, most existing work in text ranking and popularity prediction focuses on social contexts where other signals are available, which renders such works ineffective in the context of app reviews. In this work, we propose a new framework, PPrior, that enables proactive prioritization of app issues through identifying prominent reviews (ones predicted to receive a large number of votes in a given time window). Predicting highly-voted reviews is challenging given that, unlike social posts, social network features of users are not available. Moreover, there is an issue of class imbalance, since a large number of user reviews receive little to no votes. PPrior employs a pre-trained T5 model and works in three phases. Phase one adapts the pre-trained T5 model to the user reviews data in a self-supervised fashion. In phase two, we leverage contrastive training to learn a generic and task-independent representation of user reviews. Phase three uses radius neighbors classifier t o m ake t he final predictions. This phase also uses FAISS index for scalability and efficient search. To conduct extensive experiments, we acquired a large dataset of over 2.1 million user reviews from Google Play. Our experimental results demonstrate the effectiveness of the proposed framework when compared against several state-of-the-art approaches. Moreover, the accuracy of PPrior in predicting prominent reviews is comparable to that of experienced app developers.

   @inproceedings{10020586,
     author = {Fereidouni, Moghis and Mosharrof, Adib and Farooq, Umar and Siddique, A.B.},
     booktitle = {Proceedings of the 2022 IEEE International Conference on Big Data (Big Data)},
     title = {Proactive Prioritization of App Issues via Contrastive Learning},
     year = {2022},
     month = dec,
     pages = {535-544},
     doi = {10.1109/BigData55660.2022.10020586}
   }

2020

1. OOPSLA’20

   LiveDroid: Identifying and Preserving Mobile App State in Volatile Runtime Environments

   Umar Farooq, Zhijia Zhao, Manu Sridharan, and Iulian Neamtiu

   In Proc. ACM Program. Lang., Nov 2020

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   Mobile operating systems, especially Android, expose apps to a volatile runtime environment. The app state that reflects past user interaction and system environment updates (e.g., battery status changes) can be destroyed implicitly, in response to runtime configuration changes (e.g., screen rotations) or memory pressure. Developers are therefore responsible for identifying app state affected by volatility and preserving it across app lifecycles. When handled inappropriately, the app may lose state or end up in an inconsistent state after a runtime configuration change or when users return to the app. To free developers from this tedious and error-prone task, we propose a systematic solution, LiveDroid, which precisely identifies the necessary part of the app state that needs to be preserved across app lifecycles, and automatically saves and restores it. LiveDroid consists of: (i) a static analyzer that reasons about app source code and resource files to pinpoint the program variables and GUI properties that represent the necessary app state, and (ii) a runtime system that manages the state saving and recovering. We implemented LiveDroid as a plugin in Android Studio and a patching tool for APKs. Our evaluation shows that LiveDroid can be successfully applied to 966 Android apps. A focused study with 36 Android apps shows that LiveDroid identifies app state much more precisely than an existing solution that includes all mutable program variables but ignores GUI properties. As a result, on average, LiveDroid is able to reduce the costs of state saving and restoring by 16.6X (1.7X - 141.1X) and 9.5X (1.1X - 43.8X), respectively. Furthermore, compared with the manual state handling performed by developers, our analysis reveals a set of 46 issues due to incomplete state saving/restoring, all of which can be successfully eliminated by LiveDroid.

   @inproceedings{10.1145/3428228,
     author = {Farooq, Umar and Zhao, Zhijia and Sridharan, Manu and Neamtiu, Iulian},
     title = {LiveDroid: Identifying and Preserving Mobile App State in Volatile Runtime Environments},
     year = {2020},
     issue_date = {November 2020},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     volume = {4},
     number = {OOPSLA},
     url = {https://doi.org/10.1145/3428228},
     doi = {10.1145/3428228},
     booktitle = {Proc. ACM Program. Lang.},
     month = nov,
     articleno = {160},
     numpages = {30}
   }

2019

1. ASPLOS’19

   Scalable Processing of Contemporary Semi-Structured Data on Commodity Parallel Processors - A Compilation-Based Approach

   Lin Jiang, Xiaofan Sun, Umar Farooq, and Zhijia Zhao

   In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, Nov 2019

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   JSON (JavaScript Object Notation) and its derivatives are essential in the modern computing infrastructure. However, existing software often fails to process such types of data in a scalable way, mainly for two reasons: (i) the processing often requires to build a memory-consuming parse tree; (ii) there exist inherent dependences in processing the data stream, preventing any data-level parallelization. Facing the challenges, developers often have to construct ad-hoc pre-parsers to split the data stream in order to reduce the memory consumption and increase the data parallelism. However, this strategy requires more programming efforts. Moreover, the pre-parsing itself is non-trivial to parallelize, thus introducing a new serial bottleneck. To solve the dilemma, this work introduces a scalable yet fully automatic solution - a compilation system, namely JPStream, that compiles standard JSONPath queries into parallel executables with bounded memory footprints. First, JPStream adopts a stream processing design that combines the querying and parsing into one pass, without generating any in-memory parse tree. To achieve this, JPStream uses a novel joint compilation technique that compiles the queries and the JSON syntax together into a single automaton. Furthermore, JPStream leverages the "enumerability” of automaton to break the dependences and reason about the transition rules to prune infeasible states. It also features a runtime that learns structural constraints from the input to enhance the pruning. Evaluation on real-world JSON datasets with standard JSONPath queries shows that JPStream can reduce the memory consumption significantly, by up to 95%, meanwhile achieving near-linear speedup on multicore and manycore processors.

   @inproceedings{10.1145/3297858.3304008,
     author = {Jiang, Lin and Sun, Xiaofan and Farooq, Umar and Zhao, Zhijia},
     title = {Scalable Processing of Contemporary Semi-Structured Data on Commodity Parallel Processors - A Compilation-Based Approach},
     year = {2019},
     isbn = {9781450362405},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3297858.3304008},
     doi = {10.1145/3297858.3304008},
     booktitle = {Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems},
     pages = {79–92},
     numpages = {14},
     location = {Providence, RI, USA},
     series = {ASPLOS '19}
   }

2018

1. MobiSys’18

   RuntimeDroid: Restarting-Free Runtime Change Handling for Android Apps Best Paper Runner-up Award

   Umar Farooq, and Zhijia Zhao

   In Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services, Nov 2018

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   Portable devices, like smartphones and tablets, are often subject to runtime configuration changes, such as screen orientation changes, screen resizing, keyboard attachments, and language switching. When handled improperly, such simple changes can cause serious runtime issues, from data loss to app crashes.This work presents, to our best knowledge, the first formative study on runtime change handling with 3,567 Android apps. The study not only reveals the current landscape of runtime change handling, but also points out a common cause of various runtime change issues – activity restarting. On one hand, the restarting facilitates the resource reloading for the new configuration. On the other hand, it may slow down the app, and more critically, it requires developers to manually preserve a set of data in order to recover the user interaction state after restarting.Based on the findings of this study, this work further introduces a re starting-free runtime change handling solution – RuntimeDroid. RuntimeDroid can completely avoid the activity restarting, at the same time, ensure proper resource updating with user input data preserved. These are achieved with two key components: an online resource loading module, called HotR and a novel UI components migration technique. The former enables proper resources loading while the activity is still live. The latter ensures that prior user changes are carefully preserved during runtime changes.For practical use, this work proposes two implementations of RuntimeDroid: an IDE plugin and an auto-patching tool. The former allows developers to easily adopt restarting-free runtime change handling during the app developing; The latter can patch released app packages without source code. Finally, evaluation with a set of 72 apps shows that RuntimeDroid successfully fixed all the 197 reported runtime change issues, meanwhile reducing the runtime change handling delays by 9.5X on average.

   @inproceedings{10.1145/3210240.3210327,
     award = {Best Paper Runner-up Award},
     author = {Farooq, Umar and Zhao, Zhijia},
     title = {RuntimeDroid: Restarting-Free Runtime Change Handling for Android Apps},
     year = {2018},
     isbn = {9781450357203},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3210240.3210327},
     doi = {10.1145/3210240.3210327},
     booktitle = {Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services},
     pages = {110–122},
     numpages = {13},
     location = {Munich, Germany},
     series = {MobiSys '18}
   }