Computer Science

Permanent URI for this collectionhttps://hdl.handle.net/10315/38508

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Access status: Open Access ,
Deep Generative Models for Trajectory Prediction and Mobility Network Forecasting
(2025-07-23) Nadiri, Amirhossein; Papagelis, Manos
Predicting human mobility is essential for urban planning, traffic management, and epidemiology. This thesis tackles two intertwined challenges: accurately forecasting individual trajectories and inferring the resulting mobility network. First, we introduce TrajLearn, a Transformer‑based deep generative model that treats trajectories as token sequences and employs spatially constrained beam search to predict each individuals’s next k locations with high precision. Building on these forecasts, we present MobiNetForecast, which constructs and predicts the future topology of the mobility network by detecting when independently predicted trajectories intersect in space and time. Across large, real‑world datasets, our unified framework achieves up to 40% relative gains in trajectory accuracy and up to 100x improvement in contact prediction over state-of-the-art baselines. These results demonstrate that combining advanced sequence modeling with explicit contact inference offers a powerful, scalable solution for dynamic mobility network forecasting.
Access status: Open Access ,
Machine Unlearning for Mobility Data: An Algorithmic Perspective
(2025-07-23) Faraji, Ali; Papagelis, Manos
This work addresses machine unlearning for trajectory data, sequences of spatiotemporal points representing movement. Motivated by growing privacy concerns and regulations like GDPR and CCPA, which grant users the right to request deletion of their personal data from trained models (the right to be forgotten), we propose TraceHiding, an algorithmic framework that removes the influence of specific trajectories without full model retraining. TraceHiding estimates the data point importance and applies gradient updates to reverse it proportionally. The framework includes: (i) Estimating data point importance, (ii) a teacher-student architecture, and (iii) a loss function using Importance Scores to compute reversal gradients. We evaluate TraceHiding on benchmark trajectory classification datasets. Results show it outperforms strong baselines and state-of-the-art unlearning methods (Bad-T, SCRUB, NegGrad, and NegGrad+), effectively removing deleted trajectory influence, preserving retained data performance, and improving efficiency over retraining. To our knowledge, this is the first machine unlearning approach designed specifically for trajectory data.
Access status: Open Access ,
Evolving Software Ecosystems: The Role of Community Dynamics in Shaping Software Extensions
(2025-07-23) Onagh, Elmira; Nayebi, Maleknaz
As software ecosystems (SECOs) grow across domains, understanding how tools evolve and differentiate functionally is critical for innovation. This manuscript-based thesis explores the evolution of the software ecosystem and its influence on developers’ motivations to extend their software products in two ecosystems. In the first part, we focus on the evolution of open-source software by analyzing 6,983 GitHub Actions on GitHub Marketplace, revealing a widespread functional redundancy. A graph-based analysis of version histories and release patterns identifies early contributors and offers strategies to reduce duplication and align tools with emerging trends. In the second part, in collaboration with industry partners, we examined proprietary software products, focusing on functional maturity, in particular AI-related features in 116 patient-centric healthcare applications. We find that 86.21% of apps remain in early AI adoption stages, indicating limited advancement toward AI integration. Together, these studies introduce a generalizable, data-driven framework for analyzing functional evolution across domains.
Access status: Open Access ,
Use of Visual Content for Inference and Response in Q/A Forums
(2025-07-23) Ahmed, Faiz; Nayebi, Maleknaz
In the rapidly evolving landscape of developer communities, Q&A platforms serve as crucial resources for crowdsourcing developers' knowledge. A notable trend is the increasing use of images to convey complex queries more effectively. However, the current state-of-the-art method of duplicate question detection has not kept pace with this shift, which predominantly concentrates on text-based analysis. Inspired by advancements in image processing and numerous studies in software engineering illustrating the promising future of image-based communication on social coding platforms, we delved into image-based techniques for identifying duplicate questions on Stack Overflow. When focusing solely on text analysis of Stack Overflow questions and omitting the use of images, our automated models overlook a significant aspect of the question. Previous research has demonstrated the complementary nature of images to text. To address this, we implemented two methods of image analysis: first, integrating the text from images into the question text, and second, evaluating the images based on their visual content using image captions. After a rigorous evaluation of our model, it became evident that the efficiency improvements achieved were relatively modest, approximately an average of 1%. This marginal enhancement falls short of what could be deemed a substantial impact. As an encouraging aspect, our work lays the foundation for easy replication and hypothesis validation, allowing future research to build upon our approach and explore novel solutions for more effective image-driven duplicate question detection.
Access status: Open Access ,
VADViT:Vision Transformer-Driven Memory Forensics for Malicious Process Detection and Explainable Threat Attribution
(2025-07-23) Dehfouli, Yasin; Habibi Lashkari, Arash
Modern malware's increasing complexity limits traditional signature and heuristic-based detection, necessitating advanced memory forensic techniques. Machine learning offers potential but struggles with outdated feature sets, large memory data handling, and forensic explainability. To address these challenges, we propose VADViT, a vision-based transformer model that detects malicious processes by analyzing Virtual Address Descriptor (VAD) memory regions. VADViT converts these structures into Markov, entropy, and intensity-based images, classifying them using a Vision Transformer (ViT) with self-attention to enhance detection accuracy. We also introduce BCCC-MalMem-SnapLog-2025, a dataset logging process identifier (PID) for precise VAD extraction without dynamic analysis. Experimental results show 99% accuracy in binary classification and a 93% macro-average F1 score in multi-class detection. Additionally, attention-based sorting improves forensic analysis by ranking the most relevant malicious VAD regions, narrowing down the search space for forensic investigators.
Access status: Open Access ,
Application and Optimization of Prompt Engineering Techniques for Code Generation in Large Language Models
(2025-07-23) Wang, Chung-Yu; Pham, Hung Viet
Large Language Models have demonstrated remarkable capabilities across various domains, particularly in code generation and task-oriented reasoning. However, their accuracy and reliability in generating correct solutions remain a challenge due to the lack of task-specific prior knowledge and the limitations of existing prompt engineering techniques. Current state-of-the-art approaches, such as PAL, rely on manually crafted prompts and examples but often produce suboptimal results. Additionally, while numerous prompt engineering techniques have been developed to improve performance, selecting the most effective technique for a given task remains difficult since different queries exhibit varying levels of complexity. This work presents an integrated approach to enhance the application and optimization of prompt engineering for code generation. First, it introduces TITAN, a novel framework that refines language model reasoning and task execution through step-back and chain of thought prompting. TITAN eliminates the need for extensive manual task-specific instructions by leveraging analytical and code-generation capabilities, achieving state-of-the-art zero-shot performance in multiple tasks. Second, it proposes PET-Select, a prompt engineering agnostic model that classifies queries based on code complexity and dynamically selects the most suitable prompt engineering technique using contrastive learning. This approach enables Pet-Select to optimize prompt selection, leading to improved accuracy and significant reductions in token usage. Comprehensive evaluations across diverse benchmarks, including HumanEval, MBPP, and APPS, demonstrate the effectiveness of TITAN and Pet-Select. TITAN achieves up to 7.6 percent improvement over existing zero-shot methods, while Pet-Select enhances pass@1 accuracy by up to 1.9 percent and reduces token consumption by 49.9 percent. This work represents a significant advancement in optimizing prompt engineering for code generation in large language models, offering a robust and automated solution for improving performance in complex and diverse programming tasks.
Access status: Open Access ,
Explainability is a Game for Probabilistic Bisimilarity Distances
(2025-07-23) Nanah Ji, Anto; van Breugel, Franck
Software bugs cost trillions annually, requiring better bug detection tools. Testing is widely used but has limitations, especially in non-deterministic software, where code produces different outputs even with fixed inputs due to randomness and concurrency. Labelled Markov chains model randomness but suffer from state space explosion problem, where the number of states grows exponentially with system complexity. One solution is to identify behaviorally equivalent states using probabilistic bisimilarity. However, this method is not robust, small changes in probabilities can affect equivalences. To address this, probabilistic bisimilarity distances were introduced, a quantitative generalization of probabilistic bisimilarity. These distances have game-theoretic characterizations. This thesis illustrates how optimal policies, known as player's strategies, can explain distances. We formulate 1-maximal and 0-minimal policies, argue that they lead to better explanations. We present algorithms for these policies, prove an exponential lower bound for the 1-maximal algorithm, and show that symmetries simplify policies and, hence, explanations.
Access status: Open Access ,
Guiding Expert Database Tuning with Explainable AI
(2025-07-23) Chai, Andrew Brian Frederick; Szlichta, Jarek
Modern database systems, such as IBM Db2, rely on cost-based optimizers to improve workload performance. However, their decision-making processes are difficult to interpret. Tuning them for specific workloads remains challenging due to their complexity, numerous configuration options, and interaction with unique workload characteristics. Additionally, database systems increasingly rely on black-box machine learning models within the optimizer and automatic tuning tools. These black-box models lack interpretability, hindering expert trust and debugging. We propose GEX, a system that provides interpretable insights into database optimizer behavior using explainable AI techniques. We adapt XAI techniques for generating perturbation-based saliency maps from surrogate models to the domain of SQL queries. With GEX we propose a framework for how saliency scores can be used to guide experts in system tuning tasks such as statistical view creation, configuration parameter adjustment, and query rewrite. We demonstrate the ability of GEX to capture and communicate optimizer behaviour through experimental evaluation in these tasks using the TPC-DS benchmark and IBM Db2.
Access status: Open Access ,
Securing Multi-Layer Federated Learning: Detecting and Mitigating Adversarial Attacks
(2025-04-10) Gouge, Justin; Wang, Ping
Within the realm of federated learning (FL), adversarial entities can poison models, slowing down or destroying the FL training process. Therefore, attack prevention and mitigation are crucial for FL. Real-world scenarios may necessitate additional separation or abstraction between clients and servers. When considering multi-layer FL systems, which contain edge server layers, the structural differences warrant new strategies to handle adversaries. While existing works primarily address attack prevention and mitigation in conventional two-layer FL systems, research on attack prevention and mitigation in multi-layer federated learning systems remains limited. This thesis aims to address this gap by investigating the defense strategies in a multi-layered FL system. We propose new methods for anomaly detection and removal of attackers/adversarial entities from training in a multi-layer FL system. First, we train a variational autoencoder (VAE) using the model updates collected from the edge servers. This allows the VAE to discern between benign and adversarial model updates. Following that, we deploy the VAE to detect which edge servers at the cohort level contain malicious clients. Subsequently, we devise two malicious client exclusion strategies: the scoring-based method, which applies a score for each client based upon its appearances within cohorts labeled as benign or malicious, and the Bayesian-based method, which uses Bayesian inference to predict if a specific client is malicious based on the statistical performance of the autoencoder. Both approaches are aimed at mitigating potential harm caused by malicious clients during model training. The experimental results demonstrate the superiority of the proposed methods over previous works for traditional FL mitigation under a variety of scenarios.
Access status: Open Access ,
Tor User De-Anonymization: Client-Side Originating Watermark
(2025-04-10) Brown, Daniel; Vlajic, Natalija
Traditional techniques for Tor user de-anonymization through a side-channel by means of traffic-watermarks are generally implemented through utilization/modulation of server-side originating traffic (SSOW). However, the effectiveness of SSOW is often hindered by significant amounts of traffic noise that accumulates along Tor’s communication pathways. In this thesis, we outline the key ideas behind our novel user de-anonymization technique that utilizes client-side originating watermarks (CSOW). We describe some potential ways this scheme could be implemented in practice while not requiring the control of any Tor node or other resource. We also demonstrate significantly superior real-world performance of our CSOW approach vs. those previously discussed in the literature. Finally, we propose the use of Long Short-Term Memory (LSTM) DNN for the purpose of more effective watermark detection. The real-world experimentations demonstrate excellent potential of our proposed LSTM-Based CSOW watermark detection system to accurately de-anonymize Tor users while keeping the number of false positives (e.g., users mistakenly accused of wrongdoing) at an absolute 0.
Access status: Open Access ,
A Solution for Scale Ambiguity in Generative Novel View Synthesis
(2025-04-10) Forghani, Fereshteh; Brubaker, Marcus
Generative Novel View Synthesis (GNVS) involves generating plausible unseen views of a scene given an initial view and the relative camera motion between the input and target views using generative models. A key limitation of current generative methods lies in their susceptibility to scale ambiguity, an inherent challenge in multi-view datasets caused by the use of monocular techniques to estimate camera positions from uncalibrated video frames. In this work, we present a novel approach to tackle this scale ambiguity in multi-view GNVS by optimizing the scales as parameters in an end-to-end fashion. We also introduce Sample Flow Consistency (SFC), a novel metric designed to assess scale consistency across samples with the same camera motion. Through various experiments, we demonstrate our approach yields improvements in terms of SFC, providing more consistent and reliable novel view synthesis.
Access status: Open Access ,
Dual-Agent Deep Reinforcement Learning Approach to GPU Job Scheduling
(2025-04-10) Shao, Yiming; An, Aijun
Public cloud GPU clusters are increasingly used for distributed deep learning tasks, making the job scheduler critical for minimizing job waiting and completion times. However, scheduling is inherently complex and NP-hard. Current approaches typically address job scheduling and GPU allocation separately, leading to suboptimal performance. DRL-based scheduling methods, while flexible, often overlook two challenges. Firstly, they focus on minimizing the total job completion time and ignore fairness in waiting times. Secondly, distributed training speed is significantly influenced by GPU communication costs, often overlooked. To address this, we introduce AttentiveSched, a DRL-based framework that simultaneously optimizes job selection and GPU assignment. AttentiveSched considers cluster topology for informed scheduling. Its two agents (job and GPU) use attention mechanisms to capture global relationships in the input sequence. By addressing fairness, job completion time, and communication costs in its rewards, AttentiveSched outperforms heuristics-based, meta-heuristics-based, and other DRL-based schedulers on real-world datasets.
Access status: Open Access ,
From Discrete to Continuous: Learning 3D Geometry from Unstructured Points by Random Continuous Space Queries
(2025-04-10) Jia, Meng; Kyan, Matthew J.
In this dissertation, we focus on generalizing recent point convolution methods and building well-behaved point-cloud 3D shape features to achieve more robust, invariant, and versatile implicit neural representations (INR) of 3D shapes. In recent efforts to explore point-cloud based learning methods to improve 3D shape analysis, there has been much attention paid to the use of INR-based frameworks. Existing methods, however, mostly formulate models with an encoder-decoder architecture that incorporates a global shape embedding space, which often fails to model fine-grained local details efficiently, limiting overall generalization performance. To overcome this problem, we propose a convolutional feature space sampling operation (Dual-Feature Sampling or DFS) and develop a novel INR learning framework (Stochastic Continuous Function Learning or SCFL). This framework is first adapted and evaluated for its use in surface reconstruction of generic objects from sparsely sampled point clouds, which is a task that has been extensively used to bench-mark INR 3D shape learning methods. This study demonstrates impressive capabilities of our method, namely: 1) an ability to faithfully recover fine details and uncommon shape characteristics; 2) improved robustness to point-cloud rotation; 3) flexibility to handle different levels of sparsity in the input point clouds; 4) significantly better generalization in the presence of unseen shape categories. In addition, the proposed DFS operator proposed for this framework is well-formulated and general enough that it can be easily made compatible for integration into existing systems designed to address more complex 3D shape tasks. In this work, we harness this powerful ability to represent shape, within a newly proposed SCFL-based occupancy network, applied to shape based processing problems in medical image registration and segmentation. Specifically, our network is adapted and applied to two different, traditionally challenging problems: 1) liver image-to-physical registration; and 2) tumour-bearing whole brain segmentation. In both of these tasks, significant deformation can severely degrade and hinder performance. We illustrate however, that accuracy in both tasks can be considerably improved over baseline methods using our proposed network. Finally, through the course of the investigations conducted, an intensive effort has been made throughout the dissertation to review, analyze and offer speculative insights into the features of these proposed innovations, their role in the configurations presented, as well as possible utility in other scenarios and configurations that may warrant future investigation. It is our hope that the work in this dissertation may help to spark new ideas to advance the state of the art in learning-based representation of 3D shapes and encourage more interest in novel applications of INR to solve real-world problems.
Access status: Open Access ,
Automatic Instantiation of Assurance Cases from Patterns using Large Language Models
(2025-04-10) Odu, Oluwafemi John; Belle, Alvine Boaye
Justifying the correct implementation of mission-critical systems' non-functional requirements (e.g., safety, and security) is crucial to prevent system failure. The latter could have severe consequences such as the death of people and financial losses. Assurance cases can be used to prevent system failure. They are structured sets of arguments supported by evidence, demonstrating that a system’s non-functional requirements have been correctly implemented. Assurance case patterns serve as templates derived from previous successful assurance cases, aimed at facilitating the creation of new assurance cases. Despite the use of these patterns to generate assurance cases, their instantiation remains a largely manual and error-prone process that heavily relies on domain expertise. Thus, exploring techniques to support their automatic instantiation becomes crucial. To address this, our thesis explores the literature on assurance case patterns to understand recent advancements and trends characterizing that literature. Then we investigated the potential of Large Language Models (LLMs) in automating the generation of assurance cases that comply with specific assurance case patterns. Our findings suggest that LLMs can generate assurance cases that comply with the given patterns. However, this study also highlights that LLMs may struggle with understanding some nuances related to pattern-specific relationships. While LLMs exhibit potential in the automatic generation of assurance cases, their capabilities still fall short compared to human experts. Therefore, a semi-automatic approach to instantiating assurance cases may be more advisable at this time.
Access status: Open Access ,
Intruders' Behavior Unveiled: A Dual-Tier Behavior-driven Model for Malicious Activity Detection in IoT Network Using Graph Learning
(2025-04-10) Shafi, MohammadMoein; Habibi Lashkari, Arash
In recent years, IoT technology has transformed smart homes, with most households now including several IoT devices that provide convenience and automation. However, the security of these smart homes is paramount, as vulnerabilities can expose residents to risks like unauthorized access, data breaches, and operational disruption. Network-based threats pose a particularly critical risk due to the numerous vulnerabilities in wireless communication between devices, making it possible for attackers to intercept data or do malicious activities. While traditional intrusion detection systems exist, they are often ineffective in detecting zero-day attacks and lack the ability to identify malicious patterns across diverse threat scenarios due to limited diversity in their detection models. Moreover, these systems are not designed to fully detect all types of intrusions, especially those involving both external network activities and internal IoT communications among smart home devices. This gap is made worse by the challenges in creating specialized IoT datasets that cover a diverse set of malicious activities and data types, which require extensive technical knowledge, a diverse range of devices, and expertise in capturing, executing, and labeling attack scenarios. Such datasets are crucial for data-driven intrusion detection systems. Addressing these challenges, this thesis introduces a dual-tier detection system that effectively can zero-day attacks, and is designed in a way to be scalable for learning the behavior of diverse malicious activities. the proposed solution leverages data from both the smart home hub’s internet connection and the internal network communication of IoT devices to detect and profile malicious activities using a novel graph learning approach. Furthermore, to support this research, we have created the largest IoT smart home dataset, incorporating real-world data from over 50 devices and more than 100 carefully designed attack scenarios, captured over a five-month period. The analysis of this dataset and the performance of our detection model demonstrate promising results, providing a valuable resource and foundation for advancing smart home IoT security.
Access status: Open Access ,
Underwater gesture-based human-to-robot communication
(2025-04-10) Codd-Downey, Robert Frank; Jenkin, Michael
Underwater human to robot interaction presents significant challenges due to the harsh environment, including reduced visibility from suspended particulate matter and high attenuation of light and electromagnetic waves generally. Divers have developed an application-specific gesture language that has proven effective for diver-to-diver communication underwater. Given the wide acceptance of this language for underwater communication, it would seem an appropriate mechanism for diver to robot communication as well. Effective gesture recognition systems must address several challenges. Designing a gesture language involves balancing expressiveness and system complexity. Detection techniques range from traditional computer vision methods, suitable for small gesture sets, to neural networks for larger sets requiring extensive training data. Accurate gesture detection must handle noise and distinguish between repeated gestures and single gestures held for longer durations. Reliable communication also necessitates a feedback mechanism to allow users to correct miscommunications. Such systems must also deal with the need to recognize individual gesture tokens and their sequences, a problem that is hampered by the lack of large-scale labelled datasets of individual tokens and gesture sequences. Here these problems are addressed through weakly supervised learning and a sim2real approach that reduces by several orders of magnitude the effort required in obtaining the necessary labelled dataset. This work addresses this communication task by (i) developing a traditional diver and diver part recognition system (SCUBANetV1+), (ii) using this recognition within a weak supervision approach to train SCUBANetV2, a diver hand gesture recognition system, (iii) SCUBANetV2 recognizes individual gestures, and provides input to the Sim2Real trained SCUBALang LSTM network which translates temporal gesture sequences into phrases. This neural network pipeline effectively recognizes diver hand gestures in video data, demonstrating success in structured sequences. Each of the individual network components are evaluated independently, and the entire pipeline evaluated formally using imagery obtained in both the open ocean and in pool environments. As a final evaluation, the resulting system is deployed within a feedback structure and evaluated using a custom unmanned unwatered vehicle. Although this work concentrates on underwater gesture-based communication, the technology and learning process introduced here can be deployed in other environments for which application-specific gesture languages exist.
Access status: Open Access ,
Normalized Moments for Photo-realistic Style Transfer
(2025-04-10) Canham, Trevor Dalton; Brown, Michael S.
Style transfer, the operation of matching appearance features between source and target images, is a complex and highly subjective problem. Due to the profundity of the concept of artistic style, the optimal solution is ill-defined, so the variety of approaches that have been proposed represent partial solutions to varying degrees of efficiency, usability and appearance of results. In this work a photo-realistic style transfer method for image and video is proposed that is based on vision science principles and on a recent mathematical formulation for the deterministic decoupling of features. As a proxy for mimicking the effects of camera color rendering or post processing, the employed features (the first through fourth order moments of the color distribution) represent important cues for visual adaptation and pre-attentive processing. The method is evaluated on the above criteria in a series of application relevant experiments and is shown to have results of high visual quality, without spatio-temporal artifacts, and validation tests in the form of observer preference experiments show that it compared very well with the state-of-the-art (deep learning, optimal transport, etc.) The computational complexity of the algorithm is low, and a numerical implementation that is amenable for real-time video application is proposed and demonstrated. Finally, general recommendations for photo-realistic style transfer are discussed.
Access status: Open Access ,
Gaze-Contingent Multi-Modal and Multi-Sensory Applications
(2024-11-07) Vinnikov, Margarita; Allison, Robert
Gaze-contingent displays are applications that are driven by the user's gaze. They are an important tool for many multi-modal and multi-sensory domains. They can be used to precisely control the retinal image in real time to study visual control of natural behaviour through experimentation, or to improve user experience in virtual reality applications. In this dissertation, I explored the application of gaze-contingent display technology to dierent models and senses and evaluate whether such applications can be useful for simulation, psychophysical research and human-computer interaction. Specically, I have looked at the visual gaze-contingent display and an audio gaze-contingent display. I examined the effects of simulated visual defects on user's perception and control of self-motion during locomotion. I found that gaze-contingent display simulations of visual defects signicantly altered visual patterns and impaired the accuracy and precision of judgement of heading. I also examined the impact of simulating gaze-contingent depth-of-field for monocular and stereoscopic displays. The experimental data showed that the alleviation of negative eects associated with stereo displays depends on the user's age and the types of scenes that are viewed. Finally, I simulated gaze-contingent audio displays that imitated the cocktail party effect. My audio enhancement techniques turned to be very benecial for applications that have to deal with user's attention to multiple sources of sounds (speakers) such as teleconferences and social games. Finally, in this dissertation, I demonstrated that gaze-contingent systems can be used in many aspects of virtual system design and if combined together (used for multiple cues and senses) can be a very powerful tool for augmenting and improving the overall user experience.
Access status: Open Access ,
Leveraging the Power of Images: Image Recommendation to Enhance Issue Reports
(2024-11-07) Tan, Xuchen; Nayebi, Maleknaz
The trend of sharing images and image-based social networks has eventually changed the landscape of social networks. As a result, this shift has impacted social coding platforms, and previous studies showed that image sharing has become increasingly popular among software developers. However, most developers’ productivity assistance tools predominantly rely on textual content only. To enhance issue reports, this study focuses on three primary objectives: (i) identifying issue reports that benefit from image sharing and processing in Bugzilla, (ii) identifying the type of image that would improve the bug report, and (iii) conducting a comprehensive qualitative and quantitative evaluation of the tool’s performance and impact. The quantitative evaluation demonstrates that our tool achieves an average recall of 78% and an average F1-score of 74% in predicting the necessity of including image attachments in issue reports. Moreover, our qualitative evaluation of software developers showed that 75% of the developers found the overall design and recommendations of our method practically useful for issue reporting. This study, along with its associated dataset and methodology, represents the first research on recommending images to developers for enhanced issue report communication. Our results illuminate a promising trajectory for enhanced and visual productivity tools for developers.
Access status: Open Access ,
Empirical Analysis and Enhancement of Machine Learning Software Documentation
(2024-11-07) Sharuka Promodya Thirimanne; Nayebi, Maleknaz; Datta, Suprakash
As machine learning gains popularity, individuals from diverse fields and skill levels integrate it into their workflows. However, many lack software engineering experience, impacting the usability of documentation. Additionally, the current machine learning documentation and its issues are insufficiently addressed in the literature. This thesis comprises two papers. In the first paper, we compared the content and design differences of TensorFlow tutorials and analyzed the profiles of users who asked questions about them. We also developed a comprehensive taxonomy of TensorFlow documentation issues. In the second paper, we examined the potential of leveraging generative AI to augment machine learning documentation. We proposed a method to augment TensorFlow API documentation by addressing documentation-related questions using large language models. This thesis highlights the need for machine learning documentation to accommodate diverse skill levels as its use expands across domains and showcases the potential of generative AI to automate documentation augmentation.

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