Papers by Dr. Mahdi Fahmideh
Cornell University - arXiv, Jun 30, 2022
Artificial Intelligence (AI) solutions and technologies are being increasingly adopted in smart s... more 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.
Proceedings of the 1st International Workshop on Quantum Programming for Software Engineering, Nov 7, 2022
In today's software engineering, iterations, affordable en masse, form an important part of just ... more In today's software engineering, iterations, affordable en masse, form an important part of just about any system. However, not all computing resources are cheap to consume. In High-Performance (HPC) and Quantum Computing (QC), executions can consume considerable amounts of energy and time, which is reserved and used even if the very first steps in the process fail. This means that developers must assume a different attitude towards programming, and aim at error-free software before its execution. This is commonly facilitated using simulators, which are commonplace for both HPC and QC. However the fashion developers advance from one tool to another is ad-hoc, with no established software engineering guidelines, and the final step from simulators to HPC/QC is still a leap of faith, comparable to releasing software. In this paper, we propose a vision where developers can iterate in an agile fashion when developing quantum software. The iterations are defined such that when the solution is still vague in the beginning, computations are interactive and provide instant feedback, thus supporting conceptualization of the software and experimenting with new ideas. When the solution becomes more precise, more expensive computations such as quantum algorithm and hyperparameter optimization are executed in batches. CCS CONCEPTS • Software and its engineering → Software development techniques; Software prototyping;
CERN European Organization for Nuclear Research - Zenodo, Nov 8, 2022
DESCRIPTION OF PROJECT Arti cial intelligence (AI) technologies are considered important across a... more DESCRIPTION OF PROJECT Arti cial intelligence (AI) technologies are considered important across a vast array of industries including health, manufacturing, banking and retail. However, the promises of AI systems like improving productivity, reducing costs, and safety has now been considered with worries, that these complex systems might bring more ethical harm than economical good. The aim of this survey study is to get the understanding of the key principles of AI ethics and the challenges that could negatively impact the AI ethics principles. If you are a practitioner who has experience and domain knowledge in AI systems development or lawmaker having expertise in AI technologies and policies, then you are invited to participate in this research survey. Your response would be highly acknowledged. SURVEY PROCEDURE You will be requested to complete the survey questionnaire, which will take approximately 15-20 minutes. You will be asked a number of questions related to AI ethics principles, challenges and the impact of the challenges on AI ethics principles.
Cornell University - arXiv, Oct 18, 2022
IEEE Transactions on Services Computing
The International Conference on Evaluation and Assessment in Software Engineering 2022
Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. I... more Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assess the ethical capabilities of AI systems and provide best practices for further improvements.
arXiv: Software Engineering, Apr 17, 2020
Moving mission-oriented enterprise applications to cloud environments is a major IT strategic tas... more Moving mission-oriented enterprise applications to cloud environments is a major IT strategic task and requires a systematic approach. The foci of this paper are to review and examine existing cloud migration approaches from the process models perspective. To this aim, an evaluation framework is proposed and used to analyse and compare existing approaches for highlighting their features, similarities, and key differences. The survey distills the state of the art in cloud migration research and makes a rich inventory of important activities, recommendations, techniques, and concerns that are commonly involved in the migration process in one place. This enables academia and practitioners in the cloud computing community to get an overarching view of the cloud migration process. Furthermore, the survey identifies a number challenges that have not been yet addressed by existing approaches, developing opportunities for further research endeavors.
ArXiv, 2021
Blockchain has received expanding interest from various domains. Institutions, enterprises, gover... more Blockchain has received expanding interest from various domains. Institutions, enterprises, governments, and agencies are interested in Blockchain’s potential to augment their software systems. The unique requirements and characteristics of Blockchain platforms raise new challenges involving extensive enhancement to conventional software development processes to meet the needs of these domains. Software engineering approaches supporting Blockchain-oriented developments have been slow to materialize, despite proposals in the literature, and they have yet to be objectively analyzed. A critical appraisal of these innovations is crucial to identify their respective strengths and weaknesses. We present an analytical evaluation of several prominent Blockchain-oriented methods through a comprehensive, criteria-based evaluation framework. The results can be used for comparing, adapting, and developing a new generation of Blockchain-oriented software development processes and innovations.
Quantum computing systems rely on the principles of quantum mechanics to perform a multitude of c... more 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 architecturecentric 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.
Knowledge-Based Systems, 2022
Information Sciences, 2019
Substantial difficulties in adopting cloud services are often encountered during upgrades of exis... more Substantial difficulties in adopting cloud services are often encountered during upgrades of existing software systems. A reliable early stage analysis can facilitate an informed decision process of moving systems to cloud platforms. It can also mitigate risks against system quality goals. Towards this, we propose an interactive goal reasoning approach which is supported by a probabilistic layer for the precise analysis of cloud migration risks to improve the reliability of risk control. The approach is illustrated using a commercial scenario of integrating a digital document processing system to Microsoft Azure cloud platform.
Journal of Systems and Software, 2018
Moving legacy software systems to cloud platforms is an ever popular option. But, such an endeavo... more Moving legacy software systems to cloud platforms is an ever popular option. But, such an endeavour may not be hazard-free and demands a proper understanding of requirements and risks involved prior to taking any actions. The time is indeed ripe to undertake a realistic view of what migrating systems to the cloud may offer, an understanding of exceptional situations causing system quality goal failure, and insights on countermeasures. The cloud migration body of knowledge, although is useful, is dispersed over the current literature. It is hard for busy practitioners to digest, synthesize, and harness this body of knowledge into practice in a scenario of integrating legacy systems with cloud services. We address this issue by creating an innovative synergy between the approaches evidence-based software engineering and goaloriented modelling. We develop an evidential repository of commonly occurred obstacles and platform agnostic resolution tactics related to making systems cloud-enabled. The repository is further utilized during the systematic goal-obstacle elaboration of given cloud migration scenarios. The applicability of the proposed framework is also demonstrated.
Computers & Industrial Engineering, 2018
Context: The rapid prevalence and potential impact of big data analytics platforms have sparked a... more Context: The rapid prevalence and potential impact of big data analytics platforms have sparked an interest amongst different practitioners and academia. Manufacturing organisations are particularly well suited to benefit from data analytics platforms in their entire product lifecycle management for intelligent information processing, performing manufacturing activities, and creating value chains. This requires re-architecting their manufacturing legacy information systems to get integrated with contemporary data analytics platforms. A systematic re-architecting approach is required incorporating careful and thorough evaluation of goals for data analytics adoption. Furthermore, ameliorating the uncertainty of the impact the new big data architecture on system quality goals is needed to avoid cost blowout in implementation and testing phases. Objective: We propose an approach to reason about goals, obstacles, and to select suitable big data solution architecture that satisfy quality goal preferences and constraints of stakeholders at the presence of the decision outcome uncertainty. The approach will highlight situations that may impede the goals. They will be assessed and resolved to generate complete requirements of an architectural solution. Method: The approach employs goal-oriented modelling to identify obstacles causing quality goal failure and their corresponding resolution tactics. It combines fuzzy logic to explore uncertainties in solution architectures and to find an optimal set of architectural decisions for the big data enablement process of manufacturing systems. Result: The approach brings two innovations to the state of the art of big data analytics platform adoption in manufacturing systems: (i) A systematic goal-oriented modelling for exploring goals and obstacles in integrating manufacturing systems with data analytics platforms at the requirement level and (ii) A systematic analysis of the architectural decisions under uncertainty incorporating stakeholders' preferences. The efficacy of the approach is illustrated with a scenario of reengineering a hyper-connected manufacturing collaboration system to a new big data architecture.
2010 International Conference on Intelligent Systems, Modelling and Simulation, 2010
Abstract The Model Driven Architecture (MDA) is meant to facilitate system development by using m... more Abstract The Model Driven Architecture (MDA) is meant to facilitate system development by using models for representing both the¿ problem¿ and its¿ solution¿. In its ideal form, software development based on MDA would follow a development process that begins by producing models of the problem domain at a high level of abstraction, and then proceeds by gradually and automatically transforming them into executable code with the help of tools. However, MDA-based development has encountered several setbacks, the mitigation of ...
2011 FIFTH INTERNATIONAL CONFERENCE ON RESEARCH CHALLENGES IN INFORMATION SCIENCE, 2011
2010 12th International Conference on Computer Modelling and Simulation, 2010
Service-Oriented Software Engineering is based on concepts and principles for constructing comple... more Service-Oriented Software Engineering is based on concepts and principles for constructing complex enterprise systems in which services as building block of the system, are distributed in large networks. The main goal of the serviceoriented methodologies is to define a process for development and maintenance of service-based systems. Most of the Service-Oriented methodologies are not mature enough compared with traditional software development methodologies such as Object-Oriented or Component-Based. Hence, defining an evaluation framework will be useful for comparing methodologies for identifying their strengths and weaknesses, defining new methodologies or extending existing Service-Oriented methodologies. At the time being, there is no complete evaluation framework for evaluating Service-Oriented methodologies. The principal objective of this paper is to introduce a comprehensive evaluation framework for evaluating Service-Oriented methodologies. This evaluation tool is appropriate for methodology engineers to develop new methodologies, as well as project managers to select an appropriate methodology at a specific project.
IEEE Systems Journal, 2016
This work proposes a quantitative metric to analyze potential reusability of a BPEL (Business Pro... more This work proposes a quantitative metric to analyze potential reusability of a BPEL (Business Process Execution Language) Process. The approach is based on Description and Logic Mismatch Probability of a BPEL Process that will be reused within potential contexts. The mismatch probabilities have been consolidated to a metric formula for quantifying the probability of potential reuse of BPEL processes. An initial empirical evaluation suggests that the proposed metric properly predict potential reusability of BPEL processes. According to the experiment, there exists a significant statistical correlation between results of the metric and the experts' judgments. This indicates a predictive dependency between the proposed metric and potential reusability of BPEL processes as a measuring stick for this phenomena. If future studies ascertain these findings by replicating this experiment, the practical implications of such a metric are early detection of the design flaws and aiding architects to judge various design alternatives.
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Papers by Dr. Mahdi Fahmideh