Himer Avila-George

A greedy-metaheuristic 3-stage approach to construct covering arrays

Abstract Covering arrays are combinatorial designs used as test-suites in software and hardware testing. Because of their practical applications, the construction of covering arrays with a smaller number of rows is desirable. In this work we develop a greedy-metaheuristic 3-stage approach to construct covering arrays that improve some of the best-known ones. In the first stage, a covering perfect hash family is created using a metaheuristic approach; this initial array may not be complete, and so the derived covering array may have missing tuples. In the second stage, the covering perfect hash family is converted to a covering array and, in case there are missing tuples, a greedy approach completes the covering array through the addition of some rows. The third stage is an iterative postoptimization stage that combines two greedy algorithms and a metaheuristic algorithm; the greedy algorithms detect and reduce redundancy in the covering array, and the metaheuristic algorithm covers the tuples that may become uncovered after the reduction of redundancy. The effectiveness of our greedy-metaheuristic 3-stage approach is assessed through the construction of covering arrays of order four and strengths 3–6; the main results are the improvement of 9473 covering arrays of strength three, 9303 of strength four, 2150 of strength five, and 291 of strength six. To see how to apply covering arrays to real testing scenarios, the final part of this work presents the use of covering arrays of order four for setting up a composting process.

Multilayer perceptron architecture optimization using parallel computing techniques

The objective of this research was to develop a methodology for optimizing multilayer-perceptron-type neural networks by evaluating the effects of three neural architecture parameters, namely, number of hidden layers (HL), neurons per hidden layer (NHL), and activation function type (AF), on the sum of squares error (SSE). The data for the study were obtained from quality parameters (physicochemical and microbiological) of milk samples. Architectures or combinations were organized in groups (G1, G2, and G3) generated upon interspersing one, two, and three layers. Within each group, the networks had three neurons in the input layer, six neurons in the output layer, three to twenty-seven NHL, and three AF (tan-sig, log-sig, and linear) types. The number of architectures was determined using three factorial-type experimental designs, which reached 63, 2 187, and 50 049 combinations for G1, G2 and G3, respectively. Using MATLAB 2015a, a logical sequence was designed and implemented for constructing, training, and evaluating multilayer-perceptron-type neural networks using parallel computing techniques. The results show that HL and NHL have a statistically relevant effect on SSE, and from two hidden layers, AF also has a significant effect; thus, both AF and NHL can be evaluated to determine the optimal combination per group. Moreover, in the three study groups, it is observed that there is an inverse relationship between the number of processors and the total optimization time.

Automating an Image Processing Chain of the Sentinel-2 Satellite

In this paper, a chain of satellite image processing using free software libraries is proposed, to estimate biophysical parameters using data from the Sentinel-2 satellite. In particular, the processing chain proposed allows atmospheric correction, resampling and spatial cropping of satellite images. To evaluate the functionality of the developed processing chain, the sugarcane cultivation of the Mexican region of Jalisco is introduced as a case study; from the selected scene, the leaf area index (LAI) is estimated using a model based on the Gaussian Process Regression technique, which is trained employing synthetic reflectance data created utilizing the PROSAIL radiative transfer model.

Evaluation of Expert Systems Techniques for Classifying Different Stages of Coffee Rust Infection in Hyperspectral Images

In this work, the use of expert systems and hyperspectral imaging in the determination of coffee rust infection was evaluated. Three classifiers were trained using spectral profiles from different stages of infection, and the classifier based on a support vector machine provided the best performance. When this classifier was compared to visual analysis, statistically significant differences were observed, and the highest sensitivity of the selected classifier was found at early stages of infection.

PulAm: An App for Monitoring Crops

In this paper, we introduce PulAm a mobile application based on the Android operating system, which was designed as a support tool for the monitoring process of different crops. As a case study, we introduced the monitoring of sorghum crops, specifically against the “yellow sugarcane aphid.”

Improved pairwise test-suites for non-prime-power orders

Software testing has become a critical component of the modern software development process. Therefore, a lot of research has been done in this area in recent years, and as a result new algorithms, methodologies, and tools have been created. One of the most used testing strategies is pairwise testing; this technique ensures that all possible combinations of values between any two input parameters are covered by at least one test. In this work, a new algorithm called add factor and stochastic optimisation (AFSO) is used to build small pairwise test suites for non-prime-power orders. Starting from an orthogonal array of order v ∈ { 10 , 12 , 14 , 15 , 18 , 20 , 21 , 22 , 24 } , AFSO iteratively adds a factor and then reduces to zero the number of uncovered combinations by means of a simulated annealing algorithm. The results of the AFSO algorithm improved the size of 92 pairwise test suites with non-prime-power orders. One of these improved test suites is used in a real-word application to show the usefulness of the new results.

Search-Based Software Engineering for Constructing Covering Arrays

Search-based software engineering involves the application of optimisation methods to solve software engineering problems. One of the most significant difficulties in testing software systems is the effort needed to build the test suites required to validate a software system, which efficiently exposes faults. Given the importance of the software testing stage, a specific sub-area known as search-based software testing has become relevant in recent years. In this work, a search-based software testing algorithm for constructing covering arrays is proposed. A covering array is a combinatorial structure that can be used as a set of test cases. By utilising this algorithm, the authors reduce the size of 893 test suites.

Optimal shortening of uniform covering arrays

Software test suites based on the concept of interaction testing are very useful for testing software components in an economical way. Test suites of this kind may be created using mathematical objects called covering arrays. A covering array, denoted by CA(N; t, k, v), is an N × k array over Zv={0,…,v-1} with the property that every N × t sub-array covers all t-tuples of Zvt at least once. Covering arrays can be used to test systems in which failures occur as a result of interactions among components or subsystems. They are often used in areas such as hardware Trojan detection, software testing, and network design. Because system testing is expensive, it is critical to reduce the amount of testing required. This paper addresses the Optimal Shortening of Covering ARrays (OSCAR) problem, an optimization problem whose objective is to construct, from an existing covering array matrix of uniform level, an array with dimensions of (N − δ) × (k − Δ) such that the number of missing t-tuples is minimized. Two applications of the OSCAR problem are (a) to produce smaller covering arrays from larger ones and (b) to obtain quasi-covering arrays (covering arrays in which the number of missing t-tuples is small) to be used as input to a meta-heuristic algorithm that produces covering arrays. In addition, it is proven that the OSCAR problem is NP-complete, and twelve different algorithms are proposed to solve it. An experiment was performed on 62 problem instances, and the results demonstrate the effectiveness of solving the OSCAR problem to facilitate the construction of new covering arrays.