Artificial Intelligence and Machine Learning for Software Engineering:

13 Aug, 2020
Description of Research:

How can techniques from artificial intelligence and machine learning be used to improve complex software development tasks?                                            

 In the late years, researchers (and large companies, such as Microsoft, Google, and Facebook!) have been successfully applying artificial intelligence and deep learning techniques in software engineering tasks, such as finding bugs, detecting system anomalies, infering types in dynamic languages, and etc.The goal of this research line is to explore how AI can help software engineers to produce better software. There are many tribes of AI, namely Symbolists, Evolutionists, Bayesians, Kernel Conservatives, Connectionists). Our research focuses on applying these tribes to solve SE problems. We divide our AI-related research topics in two main directions: machine learning and deep learning for software engineering, and search-based software engineering.

1.1. Machine learning and Deep Learning for Software Engineering:

Software repositories archive valuable software engineering data, such as source code, execution traces, historical code changes, mailing lists, and bug reports. This data contains a wealth of information about a project’s status and history. Doing data science on software repositories, researchers can gain an empirically based understanding of software development practices, and practitioners can better manage, maintain, and evolve complex software projects.

In the recent years, the advances in Machine Learning and AI technologies, as demonstrated by the successful application of Deep Neural Networks in various domains did not go unnoticed in the field of Software Engineering. Our research topics include but not limited to

Type inference
Log engineering
Bug detection
Vulnerability detection
Software refactoring


1.2. Search-based software engineering

The development, maintenance, and testing of large software products involve many activities that are complex, expensive, and error-prone. For example, complex systems (e.g., autonomous cars) are typically built as a composition of features that tend to interact and impact one another’s behavior in unknown ways. Therefore, detecting feature interaction failures with manual testing becomes infeasible and too expensive when the number and the complexity of the features increase.

Nowadays, researchers and large tech companies use Computational Intelligence (CI) to automate expensive development activities, since more development automation would require less human resources. One of the most common ways to make such automation is the Search-Based Software Engineering (SBSE), which reformulates traditional software engineering tasks as search (optimization) problems. Then, CI algorithms (e.g., genetic algorithms, genetic programming, simulated annealing) are used to automate the process of discovering (e.g., detecting software defects) and building optimal solutions (e.g., software fixes).

SBSE is not only an academic research area, but it is achieving significant uptake in many industrial sectors. For example, Facebook uses multi-objective solvers to automatically design system-level test cases for mobile apps; Google uses multi-objective solvers for regression testing. SSBSE techniques has been also applied in the automotive domain, in satellite domain and security testing.

Our research topics include but are not limited to the following research topics:

Test Case Generation
Automated Program Repair
1.3 Research Team:

Dr. Javvad ur Rehman(Lecturer)

ORCID ID: 0000-0003-3806-5750

Cumulative Impact Factor: 9.5

Publications:

ur Rehman, Muhammad Javvad and Dass, Sarat C and Asirvadam, Vijanth S ,”An augmented sequential MCMC procedure for particle based learning in dynamical systems”,Signal Processing, volume=160, p32--44, Elsevier,2019.
ur Rehman, Muhammad Javvad and Dass, Sarat Chandra and Asirvadam, Vijanth Sagayan ,”A weighted likelihood criteria for learning importance densities in particle filtering”, EURASIP Journal on Advances in Signal Processing, 2018, number={1}, p36,Springer International Publishing,2018.
Zafar, Raheel and Dass, Sarat C and Malik, Aamir Saeed and Kamel, Nidal and Rehman, M Javvad Ur and Ahmad, Rana Fayyaz and Abdullah, Jafri Malin and Reza, Faruque ,”Prediction of human brain activity using likelihood ratio based score fusion”, IEEE access, volume={5}, p13010--13019, Conference proceedings 4,2017.
ur Rehman, Muhammad Javvad and Dass, Sarat C and Asirvadam, Vijanth Sm”A Bayesian parameter learning procedure for nonlinear dynamical systems via the ensemble Kalman filter”, 2018 IEEE 14th International Colloquium on Signal Processing \& Its Applications (CSPA), p161--166, IEEE,2018.
Zafar, Raheel and Malik, Aamir Saeed and Shuaibu, Aliyu Nuhu and ur Rehman, M Javvad and Dass, Sarat C ,”Classification of fMRI data using support vector machine and convolutional neural network” 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), p324--329, IEEE,2017.
Zafar, Raheel and Malik, Aamir Saeed and Shuaibu, Aliyu Nuhu and ur Rehman, M Javvad and Dass, Sarat C ,”Multiple trials in event related fMRI for different conditions”, 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)}, pages={315--319}, IEEE, 2017.
Rehman, M Javvad ur and Dass, Sarat Chandra and Asirvadam, Vijanth Sagayan ,”Nonlinear dynamical system identification using unscented Kalman filter”, AIP Conference Proceedings, volume=1787, number={1}, pages={020003}, AIP Publishing,2016.
Rehman, M Javvad and Dass, Sarat Chandra and Asirvadam, Vijanth Sagayan ,“Markov chain Monte Carlo (MCMC) method for parameter estimation of nonlinear dynamical systems” 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), p7-10, IEEE,2015.
ur Rehman, M Javvad and Dass, Sarat Chandra and Asirvadam, Vijanth Sagayan and Adly, Ahmed “Parameter estimation for nonlinear disease dynamical system using particle filter” 2015 International Conference on Computer, Control, Informatics and its Applications (IC3INA)}, p143--147, IEEE, 2015.
Dr. Raheel Zafar

Cumulative Impact Factor: 14.743

Publications:

Huma H. Khan, Muhammad N. Malik, Raheel Zafar, Feybi A. Goni, Abdou l mohammad G. Chofreh, Jiří J. Klemeš and Youseef Alotaibi , “Challenges for sustainable smart city development: A conceptual framework, Sustainable Development. Science Citation Index (SCI): I.F: 4.082.
R. Zafar, M. N. Malik, H. Hayat and A. S. Malik, "Decoding Brain Patterns for Colored and Grayscale Images using Multivariate Pattern Analysis," KSII Transactions on Internet and Information Systems, vol. 14, no. 4, pp. 1543-1561, 2020. DOI: 10.3837/tiis.2020.04.008. Science Citation Index (SCI): I.F: 0.711.
R Zafar, A Qayyum and W Mumtaz, “Automatic eye blink artifact removal for EEG based on a sparse coding technique for assessing major mental disorders”.  Journal of integrative neuroscience, 217-229, 2019. Science Citation Index (SCI): I.F: 1.14.
R. Zafar, Kamel, N., Naufal, M. et al. “A study of decoding human brain activities from simultaneous data of EEG and fMRI using MVPA,” Australasian Physical & Engineering Sciences in Medicine. https://doi.org/10.1007/s13246-018-06565. Springer Netherlands 2018. Science Citation Index (SCI): 1.17.
R. Zafar, S. C. Dass, and A. S. Malik, "Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion," PLOS ONE, vol. 12, p. e0178410, 2017. DOI: 10.1371/journal.pone.0178410. Science Citation Index (SCI):  2.82
R. Zafar, S. C. Dass, A. Malik, N. Kamel, J. Rehman, R. F. Ahmad, J. M. Abdullah, and F. Reza, "Prediction of human brain activity using likelihood ratio based score fusion. IEEE access 5, 13010-13019., 2017. DOI: 10.1109/ACCESS.2017.2698068. Science Citation Index (SCI): 3.24.
R. Zafar, N. Kamel, M. Naufal, A. S. Malik, S. C. Dass, R. F. Ahmad, J. M. Abdullah, and F. Reza, "Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network," Journal of Integrative Neuroscience, 16, 275-289. Science Citation Index (SCI): 0.68.
R. Zafar, A. S. Malik, N. Kamel, S. C. Dass, J. M. Abdullah, F. Reza, and A. H. A. Karim, "Decoding of visual information from human brain activity: A review of fMRI and EEG studies," Journal of integrative neuroscience, 14, 155-168, 2015.  Science Citation Index (SCI): I.F: 0.9.
R. Zafar, A. S. Malik, J. Rehman, and S. C. Dass, “Classification of fMRI data using support vector machine and convolutional neural network”, 2017 IEEE International Conference on Signal and Image Processing Applications.
R. Zafar, A. S. Malik, J. Rehman, and S. C. Dass, “Multiple trials in event related fMRI for different conditions”, 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).
R. Zafar, A. S. Malik, N. Kamel, and S. C. Dass, "Role of voxel selection and ROI in fMRI data analysis," in 2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2016, pp. 1-6.
R. Zafar, A. S. Malik, N. Kamel, and S. C. Dass, "fMRI based brain state analysis of visual activities," in 2015 IEEE Student Symposium in Biomedical Engineering & Sciences (ISSBES), 2015, pp. 46-49.
R. Zafar, A. S. Malik, N. Kamel, and S. C. Dass, "Importance of realignment parameters in fMRI data analysis," in 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 2015, pp. 546-550.
R. Zafar, A. S. Malik, H. U. Amin, N. Kamel, S. Dass, and R. F. Ahmad, "EEG Spectral Analysis during Complex Cognitive Task at Occipital," 2014.
Rana Fayyaz Ahmad, Aamir Saeed Malik, Hafeezullah Amin, Nidal Kamel, Raheel Zafar, Abdul Qayyum, “A new approach for error minimization of piezoelectric sensor output variations using fuzzy logic” in 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014).
Rana Fayyaz Ahmad, Aamir Saeed Malik, Nidal Kamel, Hafeezullah Amin, Raheel Zafar, Abdul Qayyum, Faruque Reza, “Discriminating the different human brain states with EEG signals using Fractal dimension: A nonlinear approach” in 2014  IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA). 
R. Zafar, A. S. Malik, H. U. Amin, N. Kamel, and S. C. Dass, "Discrimination of Brain States Using Wavelet and Power Spectral Density," In book: Neural Information Processing, November 2015, Edition: Volume 9492 of the series Lecture Notes in Computer Science. Publisher: Springer International Publishing. Editors: Sabri Arik, Tingwen Huang, Weng Kin Lai, Qingshan Liu.
R. Zafar and A. Ullah, “Modalities for Decoding Human Brain Activity September 2019”.  Publisher: Taylor & Francis. ISBN: Cyber-Enabled Intelligence.


Uzair Iqbal (Lecturer)

Cumulative Impact Factor: 11.1

Publications:

Uzair Iqbal, Teh Ying Wah, M. Habib Ur Rehman and Jamal Hussain Shah,” Prediction analytics of myocardial infarction through model-driven deep deterministic learning” in Neural Computing and Applications.2019 DOI: https://doi.org/10.1007/s00521-019-04400-9 (IF=4.6),2019.
Uzair Iqbal, Teh Ying Wah, M. Habib Ur Rehman and Qurat-ul-Ain Mastoi,” A deterministic approach for finding the T onset parameter of Flatten T wave in ECG” in Journal of Information Science and Engineering,35(2): 307-321.2019 (IF=0.4) 2018.
Uzair Iqbal, Teh Ying Wah, Habib Ur Rehman, M.Imran and M. Shoaib, " Deep Deterministic Learning for Pattern Recognition of Different Cardiac Diseases through the Internet of Medical Things” in Journal of Medical Systems, 2018. DOI: 10.1007/s10916-018-1107-2 (IF=2.0)
Uzair Iqbal, Teh Ying Wah , Muhammad Habib ur Rehman , Qurat-ul-Ain Mastoi “Usage of Model Driven Environment for the Classification of ECG features: A Systematic Review” IEEE ACCESS Volume 6, 2018. DOI: 10.1109/ACCESS.2018.2828882 (IF=4.4)
Qurat-ul-Ain Mastoi ,Teh Ying Wah, Ram Gopal Raj, Uzair Iqbal “Automated Diagnosis of Coronary Artery Disease: A Review and Workflow” CARDIOLOGY RESEARCH AND PRACTICE, 2018 DOI: https://doi.org/10.1155/2018/2016282.
Uzair Iqbal, Ali Javed “Review-Scrum(R-Scrum) Introduction of Model Driven Architecture (MDA) in Agile Methodology” INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH Volume 3, Issue 11, November 2014 ISSN 2277-8616,2014.