arXiv subjects

Aakash Bansal

Publications and source records attributed to Aakash Bansal.

At least 19 recordsLinked to original sources

arXiv2026

A Superposition-Based Framework for Rapid Estimation of Arbitrary Antenna-Array Patterns

Antenna array theory is a well-established field. However, a systematic approach for fast pattern estimation in arrays with arbitrary antenna locations and orientations has not yet been developed. In this paper, based on simulated (or measured) radiation patterns of a single element, we present a Superposition-Based Framework (SBF) for numerically computing array radiation patterns in which the positions and orientations of the antenna elements can be readily modified. To validate the framework, a compact dual-layer circularly polarized patch antenna at 5.02 GHz (ESA's Celeste frequency) is designed and used as an array element in an 8-element ring antenna. Using the proposed framework and the single-element far-field pattern, the array radiation pattern is computed in 13s (excluding single-element simulation time), which is at least 50 times faster than the corresponding full-wave simulation while maintaining comparable accuracy. Comparisons with CST simulations show a peak E-field error of less than 0.5% for the intended polarization. Full-wave simulations are memory- and energy-intensive, and hence, impractical for larger arrays. The proposed SBF requires low computational power, making it energy-efficient and sustainable.

physics.app-ph↗

arXiv2026

Fully 3D-Printed Wideband Metasurface Folded Reflectarray Antenna

This article presents a fully 3D-printed wideband metasurface folded reflectarray antenna (MFRA) operating in the millimeter-wave n257 band. The proposed MFRA integrates a novel polarization-rotating reflective metasurface (RMS), a compact embedded horn feed, and a polarization-selective metasurface polarization grid (MPG), all fabricated using a low-cost in-house 3D-printed method. Unlike conventional PCB-based FRAs constrained to planar unit-cell geometries, the proposed anisotropic meta-element design exploits full three-dimensional dielectric control by tailoring varying unit-cell heights. This volumetric tuning, combined with the spatial distribution of the meta-elements, enables phase compensation exceeding $400^{\circ}$ across the aperture, supporting robust wideband performance. An MFRA prototype is in-house fabricated and experimentally validated. Measured results agree well with simulations, achieving a $-10$ dB impedance bandwidth of 20.7\% (26--32 GHz) and a peak realized gain of 31.1 dBi at 28.2 GHz. The antenna exhibits sidelobe levels below $-20$ dB, cross-polarization below $-30$ dB, and a compact height-to-diameter ratio of 0.20. Stable pencil beams with an average HPBW of $3.7^{\circ}$ are maintained across the operating band. To further validate the robustness of the proposed in-house designed MFRA, a commercially manufactured RMS was also obtained, whose measured performance shows excellent agreement with the in-house 3D-printed version, confirming a cost-effective rapid-prototyping antenna solution. The proposed MFRA is a cost-effective solution for beyond 5G and 6G high-gain point-to-point mmWave wireless applications, such as fixed wireless access, near field communication, and beam focusing.

eess.SP↗

arXiv2025

Analysis of Frequency-Diverse and Dispersion Effects in Dynamic Metasurface Antenna for Holographic Sensing and Imaging

Dynamic metasurface antennas (DMAs) represent a novel approach to programmable and affordable electromagnetic wave manipulation for enhanced wireless communications, sensing, and imaging applications. Nevertheless, current DMA designs and models are usually quasi-narrowband, neglecting the versatile frequency-diverse manifestation and its utilization. This work demonstrates the frequency-diversity and dispersion operations of a representative DMA structure at the millimeter-wave band. We demonstrate flexible dispersion manipulation through dynamic holographic reconfigurability of the meta-atoms in a DMA. This effect can create distinct radiation patterns across the operating frequency band, achieving flexible frequency diversity with enhanced scanning range within a compact, reconfigurable platform. It eliminates the need for wideband systems or complex phase-shifting networks while offering an alternative to frequency-scanned static beams of traditional leaky-wave antennas. The results establish fundamental insights into modelling and utilization of dispersive effects of DMAs in next-generation near-field and far-field holographic sensing and computational holographic imaging applications.

eess.SP↗

arXiv2025

AI-Mediated Code Comment Improvement

This paper describes an approach to improve code comments along different quality axes by rewriting those comments with customized Artificial Intelligence (AI)-based tools. We conduct an empirical study followed by grounded theory qualitative analysis to determine the quality axes to improve. Then we propose a procedure using a Large Language Model (LLM) to rewrite existing code comments along the quality axes. We implement our procedure using GPT-4o, then distil the results into a smaller model capable of being run in-house, so users can maintain data custody. We evaluate both our approach using GPT-4o and the distilled model versions. We show in an evaluation how our procedure improves code comments along the quality axes. We release all data and source code in an online repository for reproducibility.

cs.SE↗

arXiv2025

Time-Modulated EM Skins for Integrated Sensing and Communications

An innovative solution, based on the exploitation of the harmonic beams generated by time-modulated electromagnetic skins (TM-EMSs), is proposed for the implementation of integrated sensing and communication (ISAC) functionalities in a Smart Electromagnetic Environment (SEME) scenario. More in detail, the field radiated by a user terminal, located at an unknown position, is assumed to illuminate a passive TM-EMS that, thanks to a suitable modulation of the local reflection coefficients at the meta-atom level of the EMS surface, simultaneously reflects towards a receiving base station (BS) a "sum" beam and a "difference" one at slightly different frequencies. By processing the received signals and exploiting monopulse radar tracking concepts, the BS both localizes the user terminal and, as a by-product, establishes a communication link with it by leveraging on the "sum" reflected beam. Towards this purpose, the arising harmonic beam control problem is reformulated as a global optimization one, which is successively solved by means of an evolutionary iterative approach to determine the desired TM-EMS modulation sequence. The results from selected numerical and experimental tests are reported to assess the effectiveness and the reliability of the proposed approach.

eess.SY↗

arXiv2025

Which Code Statements Implement Privacy Behaviors in Android Applications?

A "privacy behavior" in software is an action where the software uses personal information for a service or a feature, such as a website using location to provide content relevant to a user. Programmers are required by regulations or application stores to provide privacy notices and labels describing these privacy behaviors. Although many tools and research prototypes have been developed to help programmers generate these notices by analyzing the source code, these approaches are often fairly coarse-grained (i.e., at the level of whole methods or files, rather than at the statement level). But this is not necessarily how privacy behaviors exist in code. Privacy behaviors are embedded in specific statements in code. Current literature does not examine what statements programmers see as most important, how consistent these views are, or how to detect them. In this paper, we conduct an empirical study to examine which statements programmers view as most-related to privacy behaviors. We find that expression statements that make function calls are most associated with privacy behaviors, while the type of privacy label has little effect on the attributes of the selected statements. We then propose an approach to automatically detect these privacy-relevant statements by fine-tuning three large language models with the data from the study. We observe that the agreement between our approach and participants is comparable to or higher than an agreement between two participants. Our study and detection approach can help programmers understand which statements in code affect privacy in mobile applications.

cs.SE↗

arXiv2024

The 2024 Active Metamaterials Roadmap

Active metamaterials are engineered structures that possess novel properties that can be changed after the point of manufacture. Their novel properties arise predominantly from their physical structure, as opposed to their chemical composition and can be changed through means such as direct energy addition into wave paths, or physically changing/morphing the structure in response to both a user or environmental input. Active metamaterials are currently of wide interest to the physics community and encompass a range of sub-domains in applied physics (e.g. photonic, microwave, acoustic, mechanical, etc.). They possess the potential to provide solutions that are more suitable to specific applications, or which allow novel properties to be produced which cannot be achieved with passive metamaterials, such as time-varying or gain enhancement effects. They have the potential to help solve some of the important current and future problems faced by the advancement of modern society, such as achieving net-zero, sustainability, healthcare and equality goals. Despite their huge potential, the added complexity of their design and operation, compared to passive metamaterials creates challenges to the advancement of the field, particularly beyond theoretical and lab-based experiments. This roadmap brings together experts in all types of active metamaterials and across a wide range of areas of applied physics. The objective is to provide an overview of the current state of the art and the associated current/future challenges, with the hope that the required advances identified create a roadmap for the future advancement and application of this field.

physics.app-ph↗

arXiv2024

Structural Morphing Metasurface for Electromagnetic Beam Manipulation

The paper presents a novel concept of a 3D structural metasurface with mechanically morphing capability that can be used as a transmit- or reflect-array to manipulate electromagnetic beams for applications in RF sensing and communications for both space and ground stations. They can be controlled using low-power actuators to deform the surface profile which is then utilised as a lens or a reflector for beamforming and steering. The proposed simulated structural metasurfaces can be used for either beam-steering or to generate bespoke contour beams for satellite communication and sensing.

physics.space-ph↗

arXiv2024

Context-aware Code Summary Generation

Code summary generation is the task of writing natural language descriptions of a section of source code. Recent advances in Large Language Models (LLMs) and other AI-based technologies have helped make automatic code summarization a reality. However, the summaries these approaches write tend to focus on a narrow area of code. The results are summaries that explain what that function does internally, but lack a description of why the function exists or its purpose in the broader context of the program. In this paper, we present an approach for including this context in recent LLM-based code summarization. The input to our approach is a Java method and that project in which that method exists. The output is a succinct English description of why the method exists in the project. The core of our approach is a 350m parameter language model we train, which can be run locally to ensure privacy. We train the model in two steps. First we distill knowledge about code summarization from a large model, then we fine-tune the model using data from a study of human programmer who were asked to write code summaries. We find that our approach outperforms GPT-4 on this task.

cs.SE↗

arXiv2024

Programmer Visual Attention During Context-Aware Code Summarization

Abridged: Programmer attention represents the visual focus of programmers on parts of the source code in pursuit of programming tasks. We conducted an in-depth human study with 10 Java programmers, where each programmer generated summaries for 40 methods from five large Java projects over five one-hour sessions. We used eye-tracking equipment to map the visual attention of programmers while they wrote the summaries. We also rate the quality of each summary. We found eye-gaze patterns and metrics that define common behaviors between programmer attention during context-aware code summarization. Specifically, we found that programmers need to read significantly (p<0.01) fewer words and make significantly (p<0.03) fewer revisits to words as they summarize more methods during a session, while maintaining the quality of summaries. We also found that the amount of source code a participant looks at correlates with a higher quality summary, but this trend follows a bell-shaped curve, such that after a threshold reading more source code leads to a significant (p<0.01) decrease in the quality of summaries. We also gathered insight into the type of methods in the project that provide the most contextual information for code summarization based on programmer attention. Specifically, we observed that programmers spent a majority of their time looking at methods inside the same class as the target method to be summarized. Surprisingly, we found that programmers spent significantly less time looking at methods in the call graph of the target method. We discuss how our empirical observations may aid future studies towards modeling programmer attention and improving context-aware automatic source code summarization.

cs.SE↗

arXiv2024

A Study on Developer Behaviors for Validating and Repairing LLM-Generated Code Using Eye Tracking and IDE Actions

The increasing use of large language model (LLM)-powered code generation tools, such as GitHub Copilot, is transforming software engineering practices. This paper investigates how developers validate and repair code generated by Copilot and examines the impact of code provenance awareness during these processes. We conducted a lab study with 28 participants, who were tasked with validating and repairing Copilot-generated code in three software projects. Participants were randomly divided into two groups: one informed about the provenance of LLM-generated code and the other not. We collected data on IDE interactions, eye-tracking, cognitive workload assessments, and conducted semi-structured interviews. Our results indicate that, without explicit information, developers often fail to identify the LLM origin of the code. Developers generally employ similar validation and repair strategies for LLM-generated code, but exhibit behaviors such as frequent switching between code and comments, different attentional focus, and a tendency to delete and rewrite code. Being aware of the code's provenance led to improved performance, increased search efforts, more frequent Copilot usage, and higher cognitive workload. These findings enhance our understanding of how developers interact with LLM-generated code and carry implications for designing tools that facilitate effective human-LLM collaboration in software development.

cs.SE↗

arXiv2024

EyeTrans: Merging Human and Machine Attention for Neural Code Summarization

Neural code summarization leverages deep learning models to automatically generate brief natural language summaries of code snippets. The development of Transformer models has led to extensive use of attention during model design. While existing work has primarily and almost exclusively focused on static properties of source code and related structural representations like the Abstract Syntax Tree (AST), few studies have considered human attention, that is, where programmers focus while examining and comprehending code. In this paper, we develop a method for incorporating human attention into machine attention to enhance neural code summarization. To facilitate this incorporation and vindicate this hypothesis, we introduce EyeTrans, which consists of three steps: (1) we conduct an extensive eye-tracking human study to collect and pre-analyze data for model training, (2) we devise a data-centric approach to integrate human attention with machine attention in the Transformer architecture, and (3) we conduct comprehensive experiments on two code summarization tasks to demonstrate the effectiveness of incorporating human attention into Transformers. Integrating human attention leads to an improvement of up to 29.91% in Functional Summarization and up to 6.39% in General Code Summarization performance, demonstrating the substantial benefits of this combination. We further explore performance in terms of robustness and efficiency by creating challenging summarization scenarios in which EyeTrans exhibits interesting properties. We also visualize the attention map to depict the simplifying effect of machine attention in the Transformer by incorporating human attention. This work has the potential to propel AI research in software engineering by introducing more human-centered approaches and data.

cs.SE↗

arXiv2023

Revisiting File Context for Source Code Summarization

Source code summarization is the task of writing natural language descriptions of source code. A typical use case is generating short summaries of subroutines for use in API documentation. The heart of almost all current research into code summarization is the encoder-decoder neural architecture, and the encoder input is almost always a single subroutine or other short code snippet. The problem with this setup is that the information needed to describe the code is often not present in the code itself -- that information often resides in other nearby code. In this paper, we revisit the idea of ``file context'' for code summarization. File context is the idea of encoding select information from other subroutines in the same file. We propose a novel modification of the Transformer architecture that is purpose-built to encode file context and demonstrate its improvement over several baselines. We find that file context helps on a subset of challenging examples where traditional approaches struggle.

cs.SE↗

arXiv2023

Modeling Programmer Attention as Scanpath Prediction

This paper launches a new effort at modeling programmer attention by predicting eye movement scanpaths. Programmer attention refers to what information people intake when performing programming tasks. Models of programmer attention refer to machine prediction of what information is important to people. Models of programmer attention are important because they help researchers build better interfaces, assistive technologies, and more human-like AI. For many years, researchers in SE have built these models based on features such as mouse clicks, key logging, and IDE interactions. Yet the holy grail in this area is scanpath prediction -- the prediction of the sequence of eye fixations a person would take over a visual stimulus. A person's eye movements are considered the most concrete evidence that a person is taking in a piece of information. Scanpath prediction is a notoriously difficult problem, but we believe that the emergence of lower-cost, higher-accuracy eye tracking equipment and better large language models of source code brings a solution within grasp. We present an eye tracking experiment with 27 programmers and a prototype scanpath predictor to present preliminary results and obtain early community feedback.

cs.SE↗

arXiv2023

Statement-based Memory for Neural Source Code Summarization

Source code summarization is the task of writing natural language descriptions of source code behavior. Code summarization underpins software documentation for programmers. Short descriptions of code help programmers understand the program quickly without having to read the code itself. Lately, neural source code summarization has emerged as the frontier of research into automated code summarization techniques. By far the most popular targets for summarization are program subroutines. The idea, in a nutshell, is to train an encoder-decoder neural architecture using large sets of examples of subroutines extracted from code repositories. The encoder represents the code and the decoder represents the summary. However, most current approaches attempt to treat the subroutine as a single unit. For example, by taking the entire subroutine as input to a Transformer or RNN-based encoder. But code behavior tends to depend on the flow from statement to statement. Normally dynamic analysis may shed light on this flow, but dynamic analysis on hundreds of thousands of examples in large datasets is not practical. In this paper, we present a statement-based memory encoder that learns the important elements of flow during training, leading to a statement-based subroutine representation without the need for dynamic analysis. We implement our encoder for code summarization and demonstrate a significant improvement over the state-of-the-art.

cs.AI↗

arXiv2023

Towards Modeling Human Attention from Eye Movements for Neural Source Code Summarization

Neural source code summarization is the task of generating natural language descriptions of source code behavior using neural networks. A fundamental component of most neural models is an attention mechanism. The attention mechanism learns to connect features in source code to specific words to use when generating natural language descriptions. Humans also pay attention to some features in code more than others. This human attention reflects experience and high-level cognition well beyond the capability of any current neural model. In this paper, we use data from published eye-tracking experiments to create a model of this human attention. The model predicts which words in source code are the most important for code summarization. Next, we augment a baseline neural code summarization approach using our model of human attention. We observe an improvement in prediction performance of the augmented approach in line with other bio-inspired neural models.

cs.SE↗

arXiv2023

A Language Model of Java Methods with Train/Test Deduplication

This tool demonstration presents a research toolkit for a language model of Java source code. The target audience includes researchers studying problems at the granularity level of subroutines, statements, or variables in Java. In contrast to many existing language models, we prioritize features for researchers including an open and easily-searchable training set, a held out test set with different levels of deduplication from the training set, infrastructure for deduplicating new examples, and an implementation platform suitable for execution on equipment accessible to a relatively modest budget. Our model is a GPT2-like architecture with 350m parameters. Our training set includes 52m Java methods (9b tokens) and 13m StackOverflow threads (10.5b tokens). To improve accessibility of research to more members of the community, we limit local resource requirements to GPUs with 16GB video memory. We provide a test set of held out Java methods that include descriptive comments, including the entire Java projects for those methods. We also provide deduplication tools using precomputed hash tables at various similarity thresholds to help researchers ensure that their own test examples are not in the training set. We make all our tools and data open source and available via Huggingface and Github.

cs.SE↗

arXiv2023

Label Smoothing Improves Neural Source Code Summarization

Label smoothing is a regularization technique for neural networks. Normally neural models are trained to an output distribution that is a vector with a single 1 for the correct prediction, and 0 for all other elements. Label smoothing converts the correct prediction location to something slightly less than 1, then distributes the remainder to the other elements such that they are slightly greater than 0. A conceptual explanation behind label smoothing is that it helps prevent a neural model from becoming "overconfident" by forcing it to consider alternatives, even if only slightly. Label smoothing has been shown to help several areas of language generation, yet typically requires considerable tuning and testing to achieve the optimal results. This tuning and testing has not been reported for neural source code summarization - a growing research area in software engineering that seeks to generate natural language descriptions of source code behavior. In this paper, we demonstrate the effect of label smoothing on several baselines in neural code summarization, and conduct an experiment to find good parameters for label smoothing and make recommendations for its use.

cs.SE↗