avm

Associate Professor
Department of Computer Science
Ryerson University

LinkedIn : profile

DBLP : profile

Announcements

I am currently taking on undergraduate and graduate students with interests or expertise in my research areas. If you would like to work with me, please drop me a line.

Research Interests

My main research interest lies in the area of quantifying and mitigating risks (in the broadest sense) in Software Engineering, focusing on large-scale software systems, especially those that process and analyze Big Data. Examples of risks are numerous:

  • Related to very large databases for Big Data that have been tested improperly, resulting in defect escapes and unplanned outages;

  • Tied to non-scalable algorithms for which it is impossible to determine a root cause of system failure fast enough to preclude prolonged outages and customer dissatisfaction;

  • Connected with requirements creeping in late in the development cycle, overrunning original budget and schedule;

  • Linked to spikes in the number of defects rediscovered by clients, overloading support and maintenance personnel.

In our work, we leverage a plethora of “tools” ranging from data mining, machine learning (including deep learning), simulation, and information theory, to blockchain, Quantum, High Performance Computing, and Cloud computing.

Supporters

My work would not have been possible without the generous support of funding agencies and industrial partners.

Funding Agencies

Selected Publications

You can find a complete list of my publications on DBLP. A selected list of papers and examples of my current research interests are given below.

Quantum Computing and Software Engineering

  • A. Miranskyy, L. Zhang, J. Doliskani, 'On Testing and Debugging Quantum Software', 2021 : preprint

  • L. Zhang, A. Miranskyy, W. Rjaibi, 'Quantum Advantage and Y2K Bug: Comparison', IEEE Software, 2021 : preprint

  • A. Miranskyy, L. Zhang, J. Doliskani, 'Is Your Quantum Program Bug-Free?', International Conference on Software Engineering (ICSE’20), 2020 : preprint

    • New Ideas and Emerging Results Distinguished Paper Award

  • A. Miranskyy, L. Zhang, 'On Testing Quantum Programs', International Conference on Software Engineering (ICSE’19), 2019 : preprint

Log Analysis and Blockchain

  • M.S. Islam, W. Pourmajidi, L. Zhang, J. Steinbacher, T. Erwin, A. Miranskyy, 'Anomaly Detection in a Large-scale Cloud Platform', International Conference on Software Engineering (ICSE’21), 2021, accepted: preprint

  • J. Rand, A. Miranskyy, 'On Automatic Parsing of Log Records', International Conference on Software Engineering (ICSE’21), 2021, accepted: preprint

  • W. Pourmajidi, A.Miranskyy, J. Steinbacher, T. Erwin, D. Godwin, 'Dogfooding: use IBM Cloud services to monitor IBM Cloud infrastructure', Center for Advanced Studies on Collaborative Research: Meeting of Minds (CASCON’19), 2019 : preprint

  • W. Pourmajidi, T. Erwin, J. Steinbacher, A.Miranskyy, 'On Challenges of Cloud Monitoring', Conference of the Center for Advanced Studies on Collaborative Research: Meeting of Minds (CASCON’17), 2017 : preprint

  • A.V. Miranskyy, M. Davison, M. Reesor, and S.S. Murtaza: 'Using entropy measures for comparison of software traces', Information Sciences, 203, no. 25, 2012 : link

Blockchain and Smart Contracts

  • W. Pourmajidi, L. Zhang, J. Steinbacher, T. Erwin, A. Miranskyy, 'Immutable Log Storage as a Service on Private and Public Blockchains', 2020 : preprint

  • W. Pourmajidi, L. Zhang, J. Steinbacher, T. Erwin, A. Miranskyy, 'Immutable Log Storage as a Service', International Conference on Software Engineering (ICSE’19), 2019 : preprint

  • W. Pourmajidi, A. Miranskyy, 'Logchain: Blockchain-assisted Log Storage', IEEE International Conference on Cloud Computing (CLOUD'18), 2018 : preprint

Big Data and Software Engineering

  • S. Hoque, A.Miranskyy, 'Online and Offline Analysis of Streaming Data', International Conference On Software Architecture (ICSA 2018), 2018 : preprint

  • S. Hoque, A.Miranskyy, 'Architecture for Analysis of Streaming Data', IEEE International Conference on Cloud Engineering (IC2E’18), 2018 : preprint

  • A.V. Miranskyy, W. Hamou-Lhadj, E. Cialini, A. Larsson, 'Operational-Log Analysis for Big Data Systems: Challenges and Solutions', IEEE Software, 33, 2, 2016, 52-59 : link

  • N.H. Madhavji, A. Miranskyy, and K. Kontogiannis, 'Big Picture of Big Data Software Engineering', International Workshop on Big Data Software Engineering (BIGDSE'15), 2015, 11-14 : link, preprint

Defect Prediction, Mining Software Repositories, Testing and Maintenance

  • S. Polisetty, A. Miranskyy, A. Basar, 'On Usefulness of the Deep-Learning-Based Bug Localization Models to Practitioners', International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE'19), 2019 : preprint

  • M. Habayeb, S.S. Murtaza, A. Miranskyy, A. Bener, 'On the Use of Hidden Markov Model to Predict the Time to Fix Bugs', IEEE Transactions on Software Engineering, 2018 : link. Presented at the International Conference on Software Engineering (ICSE’18), journal-first track.

  • D. Curro, K.G. Derpanis, A. Miranskyy 'Building Usage Profiles Using Deep Neural Nets', International Conference on Software Engineering (ICSE’17), 2017 : link, preprint, dataset

  • M. Sadat, A. Bener, A. Miranskyy, 'Rediscovery Datasets: Connecting Duplicate Reports', International Conference on Mining Software Repositories (MSR’17), 2017 : link, preprint, dataset

  • B. Caglayan, B. Turhan, A. Bener, M. Habayeb, A. Miranskyy, E. Cialini, 'Merits of Organizational Metrics in Defect Prediction: An Industrial Replication', International Conference on Software Engineering (ICSE’15), 2015 : link, preprint

Patents & IP

  • W. Pourmajidi, A.V. Miranskyy, L. Zhang, A. Erwin, D. Godwin, J. Steinbacher, Feedback-Based Automatic Anomaly Detection for Cloud Platforms, IPCOM 000260742D, (2019) (IP publication) : link

  • A.V. Miranskyy, E. Cialini, 'Calculation of the amount of changes made to a software source code at the function and block level of granularity', IPCOM 000225653D, (2013) (IP publication) : link

  • A.V. Miranskyy, E. Cialini, D. Godwin, 'Selection of Customers for Operational and Usage Profiling', IPCOM 000203574D, (2011) (IP publication) : link

  • A.V. Miranskyy, D. Godwin, E. Cialini, 'A technique for estimation of confidence interval for probability of defect rediscovery', United States Patent & Trademark Office’s Patent # 8392763, (2009) : link

  • A.V. Miranskyy D. Godwin, 'Trend change analysis using inflection points detection', IPCOM 000177081D, (2008) (IP publication) : link

  • M. Davison, M.S. Gittens, D. Godwin, N.H. Madhavji, A. Miranskyy, M. Wilding, 'Computer Software Test Coverage Analysis', United States Patent & Trademark Office’s Patent # 7793267, (2006) : link

Datasets

Dissertations

  • 'Models, Techniques, and Metrics for Managing Risk in Software Engineering', Ph.D. Dissertation, Applied Mathematics, Scientific Computing, University of Western Ontario, London, Canada, 2011 : pdf

  • 'Pricing Defaultable Bonds and Options in a CIR Risk & Default Framework', M.Sc. Dissertation, Applied Mathematics, Quantitative Finance, University of Western Ontario, London, Canada, 2004

People

Current Group Members

  • Janusan Baskararajah, MSc student

  • Mohammad Saiful Islam, PhD student

  • Mushahid Khan, MSc student

  • Sheik Mamun, PhD student

  • William Pourmajidi, PhD student

  • Sarah Sohana, MSc student

  • Iwona Sokalska, PhD student

  • Mujahid Sultan, PhD candidate

  • Avinder Walia, PhD student

  • Lei Zhang, Post-doctoral researcher

Alumni

  • Jared Rand, BSc (CS), 2020

    • Project Title: 'Automatic Parsing of Logs using Machine Learning Models'

  • Mohammad Saiful Islam, MSc (CS), 2020

    • Thesis Title: 'Anomaly Detection in Cloud Components'

  • Ali Senejani, MSc (DS), 2020

    • Project Title: 'Investigating the Challenges of Building a Robust Network Intrusion Detection System Through Assessment of Features and Machine Learning Models'

  • Kristie House-Senapati, MSc (DS), 2019

    • Project Title: 'The Use of Recommender Systems for Defect Rediscoveries'

  • Iwona Sokalska, MSc (DS), 2019

    • Project Title: 'Boosting Bug Localization with Visual Input and Self-Attention'

  • William Pourmajidi, MSc (CS), 2018

    • Thesis Title: 'Scalable Blockchain-assisted Log Storage System for Cloud-generated Logs'

  • Sravya Polisetty, MSc (CS), 2018

    • Thesis Title: 'On Empirically Examining The Effectiveness Of Deep Learning-Based Bug Localization Models'

  • Sedef Akinli Kocak, PhD (ENSCIMAN), 2017

    • Dissertation Title: 'Software Energy Consumption Prediction Using Software Code Metrics'

  • Jorge Lopez, MSc (CS), 2017

    • Thesis Title: 'Speeding up calibration of Latent Dirichlet Allocation model to improve topic analysis in Software Engineering'

  • Muad Abu-Ata, MSc (DS), 2017

    • Project Title: 'Optimization of Decision Model Microsimulation in Health Care'

  • Mefta Sadat, MSc (CS), 2017

    • Thesis Title: 'On Predicting Rediscoveries of Software Defects'

  • Sokratis Tsakiltsidis, MSc (CS), 2016

    • Thesis Title: 'Predicting the time-to-deliver of software changes'

  • Zainab Al-zanbouri, MSc (CS), 2015

    • Thesis Title: 'Database Engines: Evolution of Greenness'

  • Mayy Habayeb, MEng (MIE), 2015

    • Thesis Title: 'On the Use of Hidden Markov Model to Predict the Time to Fix Bugs'

Teaching

Courses that I taught at Ryerson over the years:

  • CPS406 - Introduction to Software Engineering

  • CPS731 - Software Engineering I

  • CPS847 - Software Tools for Startups

  • CPS888 - Software Engineering

  • CP8102 & CP9102 - Computer Science Seminar

  • CP8302 - Software Metrics

  • DS8001 - Designs of Algorithms and Programming for Massive Data

Contact me

  • Email : avm <at> ryerson.ca

  • LinkedIn : profile

  • Phone : +1 (416) 979-5000 ext. 557208

  • Mail :
    Department of Computer Science
    Ryerson University
    350 Victoria St.
    Toronto, Ontario, M5B 2K3
    Canada