Generation and Analysis of Constrained Random Sampling Patterns

Random sampling is a technique for signal acquisition which is gaining popularity in practical signal processing systems. Nowadays, event-driven analog-to-digital converters make random sampling feasible in practical applications. A process of random sampling is defined by a sampling pattern, which indicates signal sampling points in time. Practical random sampling patterns are constrained by ADC characteristics and application requirements. In this paper authors introduce statistical methods which evaluate random sampling pattern generators with emphasis on practical applications. Furthermore, the authors propose a new random pattern generator which copes with strict practical limitations imposed on patterns, with possibly minimal loss in randomness of sampling. The proposed generator is compared with existing sampling pattern generators using the introduced statistical methods. It is shown that the proposed algorithm generates random sampling patterns dedicated for event-driven-ADCs better than existed sampling pattern generators. Finally, implementation issues of random sampling patterns are discussed.Comment: 29 pages, 12 figures, submitted to Circuits, Systems and Signal Processing journa

Test Set Diameter: Quantifying the Diversity of Sets of Test Cases

A common and natural intuition among software testers is that test cases need to differ if a software system is to be tested properly and its quality ensured. Consequently, much research has gone into formulating distance measures for how test cases, their inputs and/or their outputs differ. However, common to these proposals is that they are data type specific and/or calculate the diversity only between pairs of test inputs, traces or outputs. We propose a new metric to measure the diversity of sets of tests: the test set diameter (TSDm). It extends our earlier, pairwise test diversity metrics based on recent advances in information theory regarding the calculation of the normalized compression distance (NCD) for multisets. An advantage is that TSDm can be applied regardless of data type and on any test-related information, not only the test inputs. A downside is the increased computational time compared to competing approaches. Our experiments on four different systems show that the test set diameter can help select test sets with higher structural and fault coverage than random selection even when only applied to test inputs. This can enable early test design and selection, prior to even having a software system to test, and complement other types of test automation and analysis. We argue that this quantification of test set diversity creates a number of opportunities to better understand software quality and provides practical ways to increase it.Comment: In submissio

The Optimisation of Stochastic Grammars to Enable Cost-Effective Probabilistic Structural Testing

The effectiveness of probabilistic structural testing depends on the characteristics of the probability distribution from which test inputs are sampled at random. Metaheuristic search has been shown to be a practical method of optimis- ing the characteristics of such distributions. However, the applicability of the existing search-based algorithm is lim- ited by the requirement that the software’s inputs must be a fixed number of numeric values. In this paper we relax this limitation by means of a new representation for the probability distribution. The repre- sentation is based on stochastic context-free grammars but incorporates two novel extensions: conditional production weights and the aggregation of terminal symbols represent- ing numeric values. We demonstrate that an algorithm which combines the new representation with hill-climbing search is able to effi- ciently derive probability distributions suitable for testing software with structurally-complex input domains

Automatically Discovering, Reporting and Reproducing Android Application Crashes

Mobile developers face unique challenges when detecting and reporting crashes in apps due to their prevailing GUI event-driven nature and additional sources of inputs (e.g., sensor readings). To support developers in these tasks, we introduce a novel, automated approach called CRASHSCOPE. This tool explores a given Android app using systematic input generation, according to several strategies informed by static and dynamic analyses, with the intrinsic goal of triggering crashes. When a crash is detected, CRASHSCOPE generates an augmented crash report containing screenshots, detailed crash reproduction steps, the captured exception stack trace, and a fully replayable script that automatically reproduces the crash on a target device(s). We evaluated CRASHSCOPE's effectiveness in discovering crashes as compared to five state-of-the-art Android input generation tools on 61 applications. The results demonstrate that CRASHSCOPE performs about as well as current tools for detecting crashes and provides more detailed fault information. Additionally, in a study analyzing eight real-world Android app crashes, we found that CRASHSCOPE's reports are easily readable and allow for reliable reproduction of crashes by presenting more explicit information than human written reports.Comment: 12 pages, in Proceedings of 9th IEEE International Conference on Software Testing, Verification and Validation (ICST'16), Chicago, IL, April 10-15, 2016, pp. 33-4

Optimum design of a probe fed dual frequency patch antenna using genetic algorithm

Abstract: Recent research has concentrated on different designs in order to increase the bandwidth of patch antennas and thus improve functionality of wireless communication systems. An alternative approach as shown in this paper is to design a matched probe fed rectangular patch antenna which can operate at both dual frequency (1.9 GHz and 2.4 GHz) and dual polarisation. In this design there are four variables, the two dimensions of the rectangular patch, ‘a ’ and ‘b ’ and position of the probe feed ‘Xp ’ and ‘YP’. As there is not a unique solution Genetic Algorithm (GA) was applied using two objective functions for the return loss at each frequency. The antenna was then modelled using AWR software and the predicted and practical results are shown to be in good agreement. Key Words: Genetic algorithm (GA), dual frequency, dual polarisation, probe fed patch antenn