arXiv subjects

Muhammad Waseem

Publications and source records attributed to Muhammad Waseem.

At least 73 records · Page 4Linked to original sources

arXiv2024

Enhancing Holonic Architecture with Natural Language Processing for System of Systems

The complexity and dynamic nature of System of Systems (SoS) necessitate efficient communication mechanisms to ensure interoperability and collaborative functioning among constituent systems, termed holons. This paper proposes an innovative approach to enhance holon communication within SoS through the integration of Conversational Generative Intelligence (CGI) techniques. Our approach leverages advancements in CGI, specifically Large Language Models (LLMs), to enable holons to understand and act on natural language instructions. This fosters more intuitive human-holon interactions, improving social intelligence and ultimately leading to better coordination among diverse systems. This position paper outlines a conceptual framework for CGI-enhanced holon interaction, discusses the potential impact on SoS adaptability, usability and efficiency, and sets the stage for future exploration and prototype implementation.

eess.SY↗

arXiv2024

Issues and Their Causes in WebAssembly Applications: An Empirical Study

WebAssembly (Wasm) is a binary instruction format designed for secure and efficient execution within sandboxed environments -- predominantly web apps and browsers -- to facilitate performance, security, and flexibility of web programming languages. In recent years, Wasm has gained significant attention from the academic research community and industrial development projects to engineer high-performance web applications. Despite the offered benefits, developers encounter a multitude of issues rooted in Wasm (e.g., faults, errors, failures) and are often unaware of their root causes that impact the development of web applications. To this end, we conducted an empirical study that mines and documents practitioners' knowledge expressed as 385 issues from 12 open-source Wasm projects deployed on GitHub and 354 question-answer posts via Stack Overflow. Overall, we identified 120 types of issues, which were categorized into 19 subcategories and 9 categories to create a taxonomical classification of issues encountered in Wasm-based applications. Furthermore, root cause analysis of the issues helped us identify 278 types of causes, which have been categorized into 29 subcategories and 10 categories as a taxonomy of causes. Our study led to first-of-its-kind taxonomies of the issues faced by developers and their underlying causes in Wasm-based applications. The issue-cause taxonomies -- identified from GitHub and SO, offering empirically derived guidelines -- can guide researchers and practitioners to design, develop, and refactor Wasm-based applications.

cs.SE↗

arXiv2024

Prioritizing Software Requirements Using Large Language Models

Large Language Models (LLMs) are revolutionizing Software Engineering (SE) by introducing innovative methods for tasks such as collecting requirements, designing software, generating code, and creating test cases, among others. This article focuses on requirements engineering, typically seen as the initial phase of software development that involves multiple system stakeholders. Despite its key role, the challenge of identifying requirements and satisfying all stakeholders within time and budget constraints remains significant. To address the challenges in requirements engineering, this study introduces a web-based software tool utilizing AI agents and prompt engineering to automate task prioritization and apply diverse prioritization techniques, aimed at enhancing project management within the agile framework. This approach seeks to transform the prioritization of agile requirements, tackling the substantial challenge of meeting stakeholder needs within set time and budget limits. Furthermore, the source code of our developed prototype is available on GitHub, allowing for further experimentation and prioritization of requirements, facilitating research and practical application.

cs.SE↗

arXiv2024

Large Language Model Evaluation Via Multi AI Agents: Preliminary results

As Large Language Models (LLMs) have become integral to both research and daily operations, rigorous evaluation is crucial. This assessment is important not only for individual tasks but also for understanding their societal impact and potential risks. Despite extensive efforts to examine LLMs from various perspectives, there is a noticeable lack of multi-agent AI models specifically designed to evaluate the performance of different LLMs. To address this gap, we introduce a novel multi-agent AI model that aims to assess and compare the performance of various LLMs. Our model consists of eight distinct AI agents, each responsible for retrieving code based on a common description from different advanced language models, including GPT-3.5, GPT-3.5 Turbo, GPT-4, GPT-4 Turbo, Google Bard, LLAMA, and Hugging Face. Our developed model utilizes the API of each language model to retrieve code for a given high-level description. Additionally, we developed a verification agent, tasked with the critical role of evaluating the code generated by its counterparts. We integrate the HumanEval benchmark into our verification agent to assess the generated code's performance, providing insights into their respective capabilities and efficiencies. Our initial results indicate that the GPT-3.5 Turbo model's performance is comparatively better than the other models. This preliminary analysis serves as a benchmark, comparing their performances side by side. Our future goal is to enhance the evaluation process by incorporating the Massively Multitask Benchmark for Python (MBPP) benchmark, which is expected to further refine our assessment. Additionally, we plan to share our developed model with twenty practitioners from various backgrounds to test our model and collect their feedback for further improvement.

cs.SE↗

arXiv2024

ChatGPT as a Software Development Bot: A Project-based Study

Artificial Intelligence has demonstrated its significance in software engineering through notable improvements in productivity, accuracy, collaboration, and learning outcomes. This study examines the impact of generative AI tools, specifically ChatGPT, on the software development experiences of undergraduate students. Over a three-month project with seven students, ChatGPT was used as a support tool. The research focused on assessing ChatGPT's effectiveness, benefits, limitations, and its influence on learning. Results showed that ChatGPT significantly addresses skill gaps in software development education, enhancing efficiency, accuracy, and collaboration. It also improved participants' fundamental understanding and soft skills. The study highlights the importance of incorporating AI tools like ChatGPT in education to bridge skill gaps and increase productivity, but stresses the need for a balanced approach to technology use. Future research should focus on optimizing ChatGPT's application in various development contexts to maximize learning and address specific challenges.

cs.SE↗

arXiv2024

Elastic collision rates of spin-polarized fermions in two dimensions

We study the $p$-wave elastic collision rates in a two-dimensional spin-polarized ultracold Fermi gas in the presence of a $p$-wave Feshbach resonance. We derive the analytical relation of the elastic collision rate coefficient in the close vicinity of resonance when the effective range is dominant. The elastic collision rate is enhanced by an exponential scaling of $e^{-q_{r}^{2} / q_{T}^{2}}$ towards the resonance. Here, $q_{r}$ is the resonant momentum and $q_T$ is the thermal momentum. An analogous expression is derived for the case of three dimensions successfully explains the thermalization rates measurement in the recent experiment~[Phys. Rev. A 88, 012710 (2013)]. In the zero-range limit where the effective range is negligible, the elastic collision rate coefficient is proportional to temperature $T^2$ and scattering area $A_{p}^2$. In this limit, energy transfer from high to low velocity through $p$-wave collision is approximately $\sqrt{2}$ times faster compared to the three-dimensional case. We also discuss the collisional stability in the presence of three-body losses in the background scattering limit. Our results suggest that $p$-wave evaporation may be performed with improved efficiency and may provide insight into the dynamics of the system in experiments.

cond-mat.quant-gas↗

arXiv2023

Autonomous Agents in Software Development: A Vision Paper

Large Language Models (LLM) and Generative Pre-trained Transformers (GPT), are reshaping the field of Software Engineering (SE). They enable innovative methods for executing many software engineering tasks, including automated code generation, debugging, maintenance, etc. However, only a limited number of existing works have thoroughly explored the potential of GPT agents in SE. This vision paper inquires about the role of GPT-based agents in SE. Our vision is to leverage the capabilities of multiple GPT agents to contribute to SE tasks and to propose an initial road map for future work. We argue that multiple GPT agents can perform creative and demanding tasks far beyond coding and debugging. GPT agents can also do project planning, requirements engineering, and software design. These can be done through high-level descriptions given by the human developer. We have shown in our initial experimental analysis for simple software (e.g., Snake Game, Tic-Tac-Toe, Notepad) that multiple GPT agents can produce high-quality code and document it carefully. We argue that it shows a promise of unforeseen efficiency and will dramatically reduce lead-times. To this end, we intend to expand our efforts to understand how we can scale these autonomous capabilities further.

cs.SE↗

arXiv2023

Demystifying Practices, Challenges and Expected Features of Using GitHub Copilot

With the advances in machine learning, there is a growing interest in AI-enabled tools for autocompleting source code. GitHub Copilot has been trained on billions of lines of open source GitHub code, and is one of such tools that has been increasingly used since its launch in June 2021. However, little effort has been devoted to understanding the practices, challenges, and expected features of using Copilot in programming for auto-completed source code from the point of view of practitioners. To this end, we conducted an empirical study by collecting and analyzing the data from Stack Overflow (SO) and GitHub Discussions. We searched and manually collected 303 SO posts and 927 GitHub discussions related to the usage of Copilot. We identified the programming languages, Integrated Development Environments (IDEs), technologies used with Copilot, functions implemented, benefits, limitations, and challenges when using Copilot. The results show that when practitioners use Copilot: (1) The major programming languages used with Copilot are JavaScript and Python, (2) the main IDE used with Copilot is Visual Studio Code, (3) the most common used technology with Copilot is Node.js, (4) the leading function implemented by Copilot is data processing, (5) the main purpose of users using Copilot is to help generate code, (6) the significant benefit of using Copilot is useful code generation, (7) the main limitation encountered by practitioners when using Copilot is difficulty of integration, and (8) the most common expected feature is that Copilot can be integrated with more IDEs. Our results suggest that using Copilot is like a double-edged sword, which requires developers to carefully consider various aspects when deciding whether or not to use it. Our study provides empirically grounded foundations that could inform developers and practitioners, as well as provide a basis for future investigations.

cs.SE↗

arXiv2023

Quantum Painlevé II Lax Pair and Quantum (Matrix) Analogues of Classical Painlevé II equation

In this article, we present a new quantum Painlevé II Lax pair which explicitly involves the Planck constant $ \hbar $ and an arbitrary field variable $v$ so these two objects make this new pair different from Flaschka-Newell Painlevé II Lax pair and that pair appears as particular case of our's pair which consolidates the Painlevé II equation from quantum mechanical point of view. It is shown that the compatibility of quantum Painlevé II Lax pair simultaneously yields a quantum Painlevé II equation and a quantum commutation relation between field variable $v $ and independent variable $z$. We manifest that with the different choices of arbitrary field variable system reduces to its classical version, matrix Painlevé II equation and derivative matrix Painlevé II equation. Further, we construct the gauge equivalence of quantum Painlevé II Lax pair whose compatibility condition gives rise to quantum p34 equation that involves $\hbar $ with power $+1$ which makes our system more quantized as compared to the existed one that carries $\hbar $ with power $+2$.

math-ph↗

arXiv2023

Full-Stack Quantum Software in Practice: Ecosystem, Stakeholders and Challenges

The emergence of quantum computing has introduced a revolutionary paradigm capable of transforming numerous scientific and industrial sectors. Nevertheless, realizing the practical utilization of quantum software in real-world applications presents significant challenges. Factors such as variations in hardware implementations, the intricacy of quantum algorithms, the integration of quantum and traditional software, and the absence of standardized software and communication interfaces hinder the development of a skilled workforce in this domain. This paper explores tangible approaches to establishing quantum computing software development process and addresses the concerns of various stakeholders. By addressing these challenges, we aim to pave the way for the effective utilization of quantum computing in diverse fields.

quant-ph↗

arXiv2023

Practices and Challenges of Using GitHub Copilot: An Empirical Study

With the advances in machine learning, there is a growing interest in AI-enabled tools for autocompleting source code. GitHub Copilot, also referred to as the "AI Pair Programmer", has been trained on billions of lines of open source GitHub code, and is one of such tools that has been increasingly used since its launch on June 2021. However, little effort has been devoted to understanding the practices and challenges of using Copilot in programming with auto-completed source code. To this end, we conducted an empirical study by collecting and analyzing the data from Stack Overflow (SO) and GitHub Discussions. More specifically, we searched and manually collected 169 SO posts and 655 GitHub discussions related to the usage of Copilot. We identified the programming languages, IDEs, technologies used with Copilot, functions implemented, benefits, limitations, and challenges when using Copilot. The results show that when practitioners use Copilot: (1) The major programming languages used with Copilot are JavaScript and Python, (2) the main IDE used with Copilot is Visual Studio Code, (3) the most common used technology with Copilot is Node.js, (4) the leading function implemented by Copilot is data processing, (5) the significant benefit of using Copilot is useful code generation, and (6) the main limitation encountered by practitioners when using Copilot is difficulty of integration. Our results suggest that using Copilot is like a double-edged sword, which requires developers to carefully consider various aspects when deciding whether or not to use it. Our study provides empirically grounded foundations and basis for future research on the role of Copilot as an AI pair programmer in software development.

cs.SE↗

arXiv2023

Zero-photon catalysis based eight-state discrete modulated measurement-device-independent continuous-variable quantum key distribution

Zero-photon catalysis (ZPC) introduces noiseless attenuation and can be implemented by existing technologies in quantum key distribution (QKD) protocols. In this paper, we present a ZPC-based eight-state measurement-device-independent continuous-variable QKD (MDI-CV-QKD) combined with discrete modulation and reverse reconciliation. This ZPC-involved eight-state protocol shows better efficiency in terms of optimal modulation variances, secret key rates, transmission distances, tolerable excess noises, and reconciliation efficiency compared to the eight-state protocol without ZPC, the four-state protocol without ZPC, and the four-state protocol with ZPC, at a low signal-to-noise ratio (SNR).

quant-ph↗

arXiv2023

Engineering Software Systems for Quantum Computing as a Service: A Mapping Study

Quantum systems have started to emerge as a disruptive technology and enabling platforms - exploiting the principles of quantum mechanics - to achieve quantum supremacy in computing. Academic research, industrial projects (e.g., Amazon Braket), and consortiums like 'Quantum Flagship' are striving to develop practically capable and commercially viable quantum computing (QC) systems and technologies. Quantum Computing as a Service (QCaaS) is viewed as a solution attuned to the philosophy of service-orientation that can offer QC resources and platforms, as utility computing, to individuals and organisations who do not own quantum computers. To understand the quantum service development life cycle and pinpoint emerging trends, we used evidence-based software engineering approach to conduct a systematic mapping study (SMS) of research that enables or enhances QCaaS. The SMS process retrieved a total of 55 studies, and based on their qualitative assessment we selected 9 of them to investigate (i) the functional aspects, design models, patterns, programming languages, deployment platforms, and (ii) trends of emerging research on QCaaS. The results indicate three modelling notations and a catalogue of five design patterns to architect QCaaS, whereas Python (native code or frameworks) and Amazon Braket are the predominant solutions to implement and deploy QCaaS solutions. From the quantum software engineering (QSE) perspective, this SMS provides empirically grounded findings that could help derive processes, patterns, and reference architectures to engineer software services for QC.

cs.SE↗

arXiv2023

Software Architecture for Quantum Computing Systems -- A Systematic Review

Quantum computing systems rely on the principles of quantum mechanics to perform a multitude of computationally challenging tasks more efficiently than their classical counterparts. The architecture of software-intensive systems can empower architects who can leverage architecture-centric processes, practices, description languages, etc., to model, develop, and evolve quantum computing software (quantum software for short) at higher abstraction levels. We conducted a systematic literature review (SLR) to investigate (i) architectural process, (ii) modeling notations, (iii) architecture design patterns, (iv) tool support, and (iv) challenging factors for quantum software architecture. Results of the SLR indicate that quantum software represents a new genre of software-intensive systems; however, existing processes and notations can be tailored to derive the architecting activities and develop modeling languages for quantum software. Quantum bits (Qubits) mapped to Quantum gates (Qugates) can be represented as architectural components and connectors that implement quantum software. Tool-chains can incorporate reusable knowledge and human roles (e.g., quantum domain engineers, quantum code developers) to automate and customize the architectural process. Results of this SLR can facilitate researchers and practitioners to develop new hypotheses to be tested, derive reference architectures, and leverage architecture-centric principles and practices to engineer emerging and next generations of quantum software.

cs.SE↗

arXiv2023

Towards Human-Bot Collaborative Software Architecting with ChatGPT

Architecting software-intensive systems can be a complex process. It deals with the daunting tasks of unifying stakeholders' perspectives, designers' intellect, tool-based automation, pattern-driven reuse, and so on, to sketch a blueprint that guides software implementation and evaluation. Despite its benefits, architecture-centric software engineering (ACSE) inherits a multitude of challenges. ACSE challenges could stem from a lack of standardized processes, socio-technical limitations, and scarcity of human expertise etc. that can impede the development of existing and emergent classes of software (e.g., IoTs, blockchain, quantum systems). Software Development Bots (DevBots) trained on large language models can help synergise architects' knowledge with artificially intelligent decision support to enable rapid architecting in a human-bot collaborative ACSE. An emerging solution to enable this collaboration is ChatGPT, a disruptive technology not primarily introduced for software engineering, but is capable of articulating and refining architectural artifacts based on natural language processing. We detail a case study that involves collaboration between a novice software architect and ChatGPT for architectural analysis, synthesis, and evaluation of a services-driven software application. Preliminary results indicate that ChatGPT can mimic an architect's role to support and often lead ACSE, however; it requires human oversight and decision support for collaborative architecting. Future research focuses on harnessing empirical evidence about architects' productivity and exploring socio-technical aspects of architecting with ChatGPT to tackle emerging and futuristic challenges of ACSE.

cs.SE↗

arXiv2022

AI Ethics: An Empirical Study on the Views of Practitioners and Lawmakers

Artificial Intelligence (AI) solutions and technologies are being increasingly adopted in smart systems context, however, such technologies are continuously concerned with ethical uncertainties. Various guidelines, principles, and regulatory frameworks are designed to ensure that AI technologies bring ethical well-being. However, the implications of AI ethics principles and guidelines are still being debated. To further explore the significance of AI ethics principles and relevant challenges, we conducted a survey of 99 representative AI practitioners and lawmakers (e.g., AI engineers, lawyers) from twenty countries across five continents. To the best of our knowledge, this is the first empirical study that encapsulates the perceptions of two different types of population (AI practitioners and lawmakers) and the study findings confirm that transparency, accountability, and privacy are the most critical AI ethics principles. On the other hand, lack of ethical knowledge, no legal frameworks, and lacking monitoring bodies are found the most common AI ethics challenges. The impact analysis of the challenges across AI ethics principles reveals that conflict in practice is a highly severe challenge. Moreover, the perceptions of practitioners and lawmakers are statistically correlated with significant differences for particular principles (e.g. fairness, freedom) and challenges (e.g. lacking monitoring bodies, machine distortion). Our findings stimulate further research, especially empowering existing capability maturity models to support the development and quality assessment of ethics-aware AI systems.

cs.CY↗

arXiv2022

Agile Practices for Quantum Software Development: Practitioners Perspectives

Quantum software systems are emerging software engineering (SE) genre that exploit principles of quantum bits (Qubit) and quantum gates (Qgates) to solve complex computing problems that today classic computers can not effectively do in a reasonable time. According to its proponents, agile software development practices have the potential to address many of the problems endemic to the development of quantum software. However, there is a dearth of evidence confirming if agile practices suit and can be adopted by software teams as they are in the context of quantum software development. To address this lack, we conducted an empirical study to investigate the needs and challenges of using agile practices to develop quantum software. While our semi-structured interviews with 26 practitioners across 10 countries highlighted the applicability of agile practices in this domain, the interview findings also revealed new challenges impeding the effective incorporation of these practices. Our research findings provide a springboard for further contextualization and seamless integration of agile practices with developing the next generation of quantum software.

cs.SE↗

arXiv2022

Quantifying the Risk of Wildfire Ignition by Power Lines under Extreme Weather Conditions

Utilities in California conduct Public Safety Power Shut-offs (PSPSs) to eliminate the elevated chances of wildfire ignitions caused by power lines during extreme weather conditions. We propose Wildfire Risk Aware operation planning Problem (WRAP), which enables system operators to pinpoint the segments of the network that should be de-energized. Sustained wind and wind gust can lead to conductor clashing, which could ignite surrounding vegetation. The 3D non-linear vibration equations of power lines are employed to generate a dataset that considers physical, structural, and meteorological parameters. With the help of machine learning techniques, a surrogate model is obtained which quantifies the risk of wildfire ignition by individual power lines under extreme weather conditions. The cases illustrate the superior performance of WRAP under extreme weather conditions in mitigating wildfire risk and serving customers compared to the naive PSPS approach and another method in the literature. Cases are also designated to sensitivity analysis of WRAP to critical load-serving control parameters in different weather conditions. Finally, a discussion is provided to explore our wildfire risk monetization approach and its implications for WRAP decisions.

stat.AP↗