Machine Learning Model to Predict Automated Testing Adoption

Software testing is an activity conducted to test the software under test. It has two approaches: manual testing and automation testing. Automation testing is an approach of software testing in which programming scripts are written to automate the process of testing. There are some software development projects under development phase for which automated testing is suitable to use and other requires manual testing. It depends on factors like project requirements nature, team which is working on the project, technology on which software is developing and intended audience that may influence the suitability of automated testing for certain software development project. In this paper we have developed machine learning model for prediction of automated testing adoption. We have used chi-square test for finding factors’ correlation and PART classifier for model development. Accuracy of our proposed model is 93.1624%.

Enhanced Epileptic Seizure diagnosis using EEG Signals with Support vector machine and Bagging Classifiers

Many approaches have been proposed using Electroencephalogram (EEG) to detect epilepsy seizures in their early stages. Epilepsy seizure is a severe neurological disease. Practitioners continue to rely on manual testing of EEG signals. Artificial intelligence (AI) and Machine Learning (ML) can effectively deal with this problem. ML can be used to classify EEG signals employing feature extraction techniques. This work focuses on automated detection for epilepsy seizures using ML techniques. Various algorithms are investigated, such as Bagging, Decision Tree (DT), Adaboost, Support vector machine (SVM), K-nearest neighbors(KNN), Artificial neural network(ANN), Naïve Bayes, and Random Forest (RF) to distinguish injected signals from normal ones with high accuracy. In this work, 54 Discrete wavelet transforms (DWTs) are used for feature extraction, and the similarity distance is applied to identify the most powerful features. The features are then selected to form the features matrix. The matrix is subsequently used to train ML. The proposed approach is evaluated through different metrics such as F-measure, precision, accuracy, and Recall. The experimental results show that the SVM and Bagging classifiers in some data set combinations, outperforming all other classifiers

Research and Application of Remote Control Test Method of Amplifier Indicators

As we all know, there are many test indicators for amplifiers, including saturated output power, 1dB compressed output power (P-1), harmonic suppression, etc. The saturated output power and 1dB compressed output power are tested step by step, and the harmonic suppression needs to traverse every frequency point to calculate the results, which require testers to pay more time and effort. When there are batches of power amplifiers to be tested, manual testing methods are unrealistic. In order to simplify the test steps, reduce the input of testers, and speed up the test progress, this article introduces a test method about instrument remote control, based on GPIB and C# after the study and research for the GPIB bus of the GPIB instrument. This method calls the VISA library to drive the GPIB card to communicate with the test instrument by SCPI programmable instrument standard commands and the respective remote control commands of each instrument to perform mixed programming with C#. Finally, we can complete the measurement, display and get the data by controlling the GPIB instruments on the PC side.

Application of 3D Dynamic Image Technology in Industrial Products

Aiming at the problems of manual testing of industrial products, a measurement method of industrial products based on three-dimensional dynamic imaging technology is proposed. The products on the production line are dynamically photographed from different angles and within a certain period of time by using cameras. Then the obtained Image denoising processing and contour tracking based on chain code table and line segment table to obtain boundary information and regional information of each enclosed area of the image. Experimental tests show that the test accuracy of this method is 100%, which is suitable for real-time detection. Fully automated research on product testing provides the foundation.

Fully automated functional fuzzing of Android apps for detecting non-crashing logic bugs

Android apps are GUI-based event-driven software and have become ubiquitous in recent years. Obviously, functional correctness is critical for an app’s success. However, in addition to crash bugs, non-crashing functional bugs (in short as “non-crashing bugs” in this work) like inadvertent function failures, silent user data lost and incorrect display information are prevalent, even in popular, well-tested apps. These non-crashing functional bugs are usually caused by program logic errors and manifest themselves on the graphic user interfaces (GUIs). In practice, such bugs pose significant challenges in effectively detecting them because (1) current practices heavily rely on expensive, small-scale manual validation ( the lack of automation ); and (2) modern fully automated testing has been limited to crash bugs ( the lack of test oracles ). This paper fills this gap by introducing independent view fuzzing , a novel, fully automated approach for detecting non-crashing functional bugs in Android apps. Inspired by metamorphic testing, our key insight is to leverage the commonly-held independent view property of Android apps to manufacture property-preserving mutant tests from a set of seed tests that validate certain app properties. The mutated tests help exercise the tested apps under additional, adverse conditions. Any property violations indicate likely functional bugs for further manual confirmation. We have realized our approach as an automated, end-to-end functional fuzzing tool, Genie. Given an app, (1) Genie automatically detects non-crashing bugs without requiring human-provided tests and oracles (thus fully automated ); and (2) the detected non-crashing bugs are diverse (thus general and not limited to specific functional properties ), which set Genie apart from prior work. We have evaluated Genie on 12 real-world Android apps and successfully uncovered 34 previously unknown non-crashing bugs in their latest releases — all have been confirmed, and 22 have already been fixed. Most of the detected bugs are nontrivial and have escaped developer (and user) testing for at least one year and affected many app releases, thus clearly demonstrating Genie’s effectiveness. According to our analysis, Genie achieves a reasonable true positive rate of 40.9%, while these 34 non-crashing bugs could not be detected by prior fully automated GUI testing tools (as our evaluation confirms). Thus, our work complements and enhances existing manual testing and fully automated testing for crash bugs.

Spasticity syndrome in cerebral pathology: evaluation and clinical models

Knowing the frequency of spasticity patterns in different muscles allows correcting the botulinum neurotoxin (BoNT) administration schemes and creating spasticity models that could predict the drug consumption and treatment cost.Objective: to develop clinical spasticity models based on the frequencies of the spastic syndrome in the muscles of the extremities in post-stroke patients to optimize BoNT administration.Patients and methods. We examined 129 patients of both sexes aged 61.2±8.0 years with post-stroke spasticity (mean time after the stroke – 4.6±2.2). Twenty-seven muscles were tested for spasticity: shoulder girdle (n=3), upper (n=9) and lower (n=15) extremities. We used the original manual testing methods (MTM) of spasticity and the Tardieu scale (TS).Results and discussion. We observed the following frequencies of spasticity in the arm muscles: pectoralis major, brachioradialis, pronator teres, fl. carpi radialis, fl. digitorum profundus et superfacialis, fl. pollicis long. – over 70%, subscapularis – 61%, brachialis – 56.6%, biceps brachii – 35.8%. Frequencies of spasticity in the leg muscles were: semitendinosus, semimembranosus, fl. digitorum long. – 37.5%, gracilis – 21.4%, cap. med. gastrocnemius – 48%, tibialis post. – 39.2%, soleus – 19.6%, fl. halluces long. – 23%. There was no spasticity in the hip adductors; low spasticity incidence was seen in fl. digitorum brev. et fl. halluces brev. (<10%), tibialis ant., rectus femoris (<5%); biceps femoris, teres major, fl. carpi ulnaris, and cap. lat. gastrocnemius (<2%). Based on the frequency of identified spastic patterns, we created four models of patients with arm spasticity and five models – with leg spasticity with the calculation of the necessary doses of BoNT.Conclusion. We propose several spasticity models, which allow calculating the treatment costs, considering the frequency of involvement of specific muscles in spasticity evaluation, and tracking the rehabilitation follow-up of the patient's transition from one clinical model to another.

CRITICAL ANALYSIS OF PENETRATION TESTING PROCESS AND TOOLS

Information is more susciptible than ever, and each technology advancement creates a new security issue that necessitates a new approach to solving the problem. Penetration testing is used to assess the security of an IT infrastructure by exposing its vulnerabilities in a safe manner. It also aids in acquiring access to the effectiveness of existing defense systems, tactics, and policies. The Penetration testing is carried out on a regular basis in order to detect and control risks to achieve ethics to be possessed by the testing crew involved in penetration test. This research uses a qualitative research methodology for investigating manual testing and automated testing. It further aims at critically investigation penetration testing and its importance with tools available for it. KEYWORDS: IT Security, Penetration test, IT governance, Vulnerability assessment.

The Impact of NLP on Software Testing

The number of applications being built and deployed everyday are increasing by leaps and bounds. To ensure the best user/client experience, the application needs to be free of bugs and other service issues. This marks the importance of testing phase in application development and deployment phase. Basically, testing is dissected into couple of parts being Manual Testing and Automation Testing. Manual testing, which is usually, an individual tester is given software guidance to execute. The tester would post the findings as “passed” or “failed” as per the guidance. But this kind of testing is very costly and time taking process. To eliminate these short comings, automation testing was introduced but it had very little scope and applications are limited. Now, that Artificial Intelligence has been foraying into many domains and has been showing significant impact over those domains. The core principles of Natural Language Processing that can be used in Software Testing are discussed in this paper. It also provides a glimpse at how Natural Language Processing and Software Testing will evolve in the future. Here we focus mainly on test case prioritization, predicting manual test case failure and generation of test cases from requirements utilizing NLP. The research indicates that NLP will improve software testing outcomes, and NLP-based testing will usher in a coming age of software testers work in the not-too-distant times.

Testing Asynchronous Reactive Systems: Beyond the ioco framework

Manual testing can be rather time consuming and prone to errors specially when testing asynchronous reactive systems. Model based testing is a well-established approach to verify reactive systems specified by input output labeled transition systems (IOLTSs). One of the challenges stemming from model based testing is verifying conformance and, also, generating test suites, primarily when completeness is a required property. In order to check whether an implementation under test is in compliance with its respective specification one resorts to some form of conformance relation that guarantees the expected behavior of the implementations, given the behavior of the specification. The ioco relation is an example of such a conformance relation. In this work we study another conformance relation based on formal languages. We also investigate how to generate finite and complete test suites for IOLTS models, and discuss the complexity of the test generation mechanism under this new conformance relation. We also show that ioco is a special case of this new conformance relation. Further, we relate our contributions to more recent works, accommodating the restrictions of their classes of fault models as special cases, and we expose the complexity of generating any complete test suite that must satisfy their restrictions.