Empirical Assessment of Machine Learning Techniques for Software Requirements Risk Prediction

[EN] Software risk prediction is the most sensitive and crucial activity of Software Development Life Cycle (SDLC). It may lead to success or failure of a project. The risk should be predicted earlier to make a software project successful. A Model is proposed for the prediction of software requirement risks using requirement risk dataset and machine learning techniques. Also, a comparison is done between multiple classifiers that are K-Nearest Neighbour (KNN), Average One Dependency Estimator (A1DE), Naïve Bayes (NB), Composite Hypercube on Iterated Random Projection (CHIRP), Decision Table (DT), Decision Table/ Naïve Bayes Hybrid Classifier (DTNB), Credal Decision Trees (CDT), Cost-Sensitive Decision Forest (CS-Forest), J48 Decision Tree (J48), and Random Forest (RF) to achieve best suited technique for the model according to the nature of dataset. These techniques are evaluated using various evaluation metrics including CCI (correctly Classified Instances), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), precision, recall, F-measure, Matthew¿s Correlation Coefficient (MCC), Receiver Operating Characteristic Area (ROC area), Precision-Recall Curves area (PRC area), and accuracy. The inclusive outcome of this study shows that in terms of reducing error rates, CDT outperforms other techniques achieving 0.013 for MAE, 0.089 for RMSE, 4.498% for RAE, and 23.741% for RRSE. However, in terms of increasing accuracy, DT, DTNB and CDT achieve better results.This work was supported by by Generalitat Valenciana, Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital, (project AICO/019/224)Naseem, R.; Shaukat, Z.; Irfan, M.; Shah, MA.; Ahmad, A.; Muhammad, F.; Glowacz, A.... (2021). Empirical Assessment of Machine Learning Techniques for Software Requirements Risk Prediction. Electronics. 10(2):1-19. https://doi.org/10.3390/electronics1002016811910

Outliagnostics: Visualizing Temporal Discrepancy in Outlying Signatures of Data Entries

This paper presents an approach to analyzing two-dimensional temporal datasets focusing on identifying observations that are significant in calculating the outliers of a scatterplot. We also propose a prototype, called Outliagnostics, to guide users when interactively exploring abnormalities in large time series. Instead of focusing on detecting outliers at each time point, we monitor and display the discrepant temporal signatures of each data entry concerning the overall distributions. Our prototype is designed to handle these tasks in parallel to improve performance. To highlight the benefits and performance of our approach, we illustrate and validate the use of Outliagnostics on real-world datasets of various sizes in different parallelism configurations. This work also discusses how to extend these ideas to handle time series with a higher number of dimensions and provides a prototype for this type of datasets.Comment: in IEEE Visualization in Data Science (IEEE VDS) (2019

An L-infinity Norm Visual Classifier

Abstract—We introduce a mathematical framework, based on the L ∞ norm distance metric, to describe human interactions in a visual data mining environment. We use the framework to build a classifier that involves an algebra on hyper-rectangles. Our classifier, called VisClassifier, generates set-wise rules from simple gestures in an exploratory visual GUI. Logging these rules allows us to apply our analysis to a new sample or batch of data so that we can assess the predictive power of our visual-processing motivated classifier. The accuracy of this classifier on widely-used benchmark datasets rivals the accuracy of competitive classifiers. Keywords-Visual data mining; Supervised classification; I