A
Ali Ouni
Researcher at Université du Québec
Publications - 137
Citations - 3984
Ali Ouni is an academic researcher from Université du Québec. The author has contributed to research in topics: Code refactoring & Computer science. The author has an hindex of 28, co-authored 108 publications receiving 2514 citations. Previous affiliations of Ali Ouni include United Arab Emirates University & Rochester Institute of Technology.
Papers
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Journal ArticleDOI
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †
TL;DR: A LSTM model using only optimally selected time lagged features captured all the characteristics of complex time series and showed decreased Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for medium to long range forecasting for a wider metropolitan area.
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Many-Objective Software Remodularization Using NSGA-III
Wiem Mkaouer,Marouane Kessentini,Adnan Shaout,Patrice Koligheu,Slim Bechikh,Kalyanmoy Deb,Ali Ouni +6 more
TL;DR: A novel many-objective search-based approach using NSGA-III that aims at finding the optimal remodularization solutions that improve the structure of packages, minimize the number of changes, preserve semantics coherence, and reuse the history of changes.
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Do developers update their library dependencies
TL;DR: In this paper, the authors investigate the extent of which developers update their library dependencies and find that even though third-party reuse is common practice, updating a dependency is not as common for many developers.
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Do Developers Update Their Library Dependencies? An Empirical Study on the Impact of Security Advisories on Library Migration
TL;DR: In this paper, the authors investigate the extent of which developers update their library dependencies and find that even though third-party reuse is commonplace, the practice of updating a dependency is not as common for many developers.
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Maintainability defects detection and correction: a multi-objective approach
TL;DR: This paper proposes a two-step automated approach to detect and then to correct various types of maintainability defects in source code, using Genetic Programming to allow automatic generation of rules to detect defects, thus relieving the designer from a fastidious manual rule definition task.