Automation Of Testing Process Research Articles

Evaluating the Decision to Implement Fully Automated Testing to Increase Business Quality Service in Asuransi Astra Digital Products

Taking these developments into account, this research will focus on assessing the ability of Asuransi Astra's digital teams to implement a fully automated testing process for its digital products. By analyzing various factors that can influence the successful implementation of automated testing, this research aims to provide insights in determining what and how many features requires to be selected to be tested by automation within Asuransi Astra's digital products. The aim of this research is to evaluate the discision to implement fully automated testing to increase bussines quality services in asuransi astra digital products. After determining the research design, the author requires data to assist in answering the research questions and achieving the goals. There is a process known as data collection for gathering necessary data. The model has facilitated understanding role expectations and aligned team efforts, allowing for a more synchronized approach towards teamwork and strategy.

A Tablet-Based Aphasia Assessment System "STELA": Feasibility and Validation Study.

There is an extensive library of language tests, each with excellent psychometric properties; however, many of the tests available take considerable administration time, possibly bearing psychological strain on patients. The Short and Tailored Evaluation of Language Ability (STELA) is a simplified, tablet-based language ability assessment system developed to address this issue, with a reduced number of items and automated testing process. The aim of this paper is to assess the administration time, internal consistency, and validity of the STELA. The STELA consists of a tablet app, a microphone, and an input keypad for clinician's use. The system is designed to assess language ability with 52 questions grouped into 2 comprehension modalities (auditory comprehension and reading comprehension) and 3 expression modalities (naming and sentence formation, repetition, and reading aloud). Performance in each modality was scored as the correct answer rate (0-100), and overall performance expressed as the sum of modality scores (out of 500 points). The time taken to complete the STELA was significantly less than the time for the WAB (mean 16.2, SD 9.4 vs mean 149.3, SD 64.1 minutes; P<.001). The STELA's total score was strongly correlated with the WAB Aphasia Quotient (r=0.93, P<.001), supporting the former's concurrent validity concerning the WAB, which is a gold-standard aphasia assessment. Strong correlations were also observed at the subscale level; STELA auditory comprehension versus WAB auditory comprehension (r=0.75, P<.001), STELA repetition versus WAB repetition (r=0.96, P<.001), STELA naming and sentence formation versus WAB naming and word finding (r=0.81, P<.001), and the sum of STELA reading comprehension or reading aloud versus WAB reading (r=0.82, P<.001). Cronbach α obtained for each modality was .862 for auditory comprehension, .872 for reading comprehension, .902 for naming and sentence formation, .787 for repetition, and .892 for reading aloud. Global Cronbach α was .961. The average of the values of item-total correlation to each subscale was 0.61 (SD 0.17). Our study confirmed significant time reduction in the assessment of language ability and provided evidence for good internal consistency and validity of the STELA tablet-based aphasia assessment system.

Multiobjective Genetic Algorithm for Class Testing using OCL Class Contract Specifications: A Framework

It has been a software trend to build large-scale complex systems with high reliability. Due to the size of the software and the dynamic requirements of the stakeholders, it becomes hard to test those software systems manually. This may lead the software to fatal failures and cause irrecoverable catastrophic damage. To be safe, the software system must be investigated thoroughly before it is too late. Test sequence generation for Unified Modeling Language (UML) class models from their semiformal Object Constraint Language specifications can be helpful in identifying the defects in the early phase of the software life cycle. The existing approaches suffer from inherent problems of exhaustive exploration of finite state machines (infeasible paths, exponential number of test sequences, and uncertainty of completion of testing). Evolutionary algorithms can greatly help by optimizing the test sequences to get optimal coverage, minimal cost, and higher quality. The proposed approach helps us to improve the testing of Unified Modeling Language (UML) model-based software, by testing the conformance to semiformal class operation contract specifications (specified in the form of Object Management Group (OMG) standard and Object Constraint Language (OCL) semiformal language). The presented research achieved two main goals: (1) automation of testing process and conformance to standards of the current technique of test sequence generation, bridging the gap between the research and industry; (2) improvement in the state of the art approach through the application of multiobjective genetic algorithms (MOGAs). A case study along with the results achieved through the proposed technique is presented as well, clearly reflecting the significance of the proposed research.

Robotic Automation of Software Testing From a Machine Learning Viewpoint

The need to scale software test automation while managing the test automation process within a reasonable time frame remains a crucial challenge for software development teams (DevOps). Unlike hardware, the software cannot wear out but can fail to satisfy the functional requirements it is supposed to meet due to the defects observed during system operation. In this era of big data, DevOps teams can deliver better and efficient code by utilizing machine learning (ML) to scan their new codes and identify test coverage gaps. While still in its infancy, the inclusion of ML in software testing is a reality and requirement for coming industry demands. This study introduces the prospects of robot testing and machine learning to manage the test automation process to guarantee software reliability and quality within a reasonable timeframe. Although this paper does not provide any particular demonstration of ML-based technique and numerical results from ML-based algorithms, it describes the motivation, possibilities, tools, components, and examples required for understanding and implementing the robot test automation process approach.

Detecting Process Anomalies in the GMAW Process by Acoustic Sensing with a Convolutional Neural Network (CNN) for Classification

Today, the quality of welded seams is often examined off-line with either destructive or non-destructive testing. These test procedures are time-consuming and therefore costly. This is especially true if the welds are not welded accurately due to process anomalies. In manual welding, experienced welders are able to detect process anomalies by listening to the sound of the welding process. In this paper, an approach to transfer the “hearing” of an experienced welder into an automated testing process is presented. An acoustic measuring device for recording audible sound is installed for this purpose on a fully automated welding fixture. The processing of the sound information by means of machine learning methods enables in-line process control. Existing research results until now show that the arc is the main sound source. However, both the outflow of the shielding gas and the wire feed emit sound information. Other investigations describe welding irregularities by evaluating and assessing existing sound recordings. Descriptive analysis was performed to find a connection between certain sound patterns and welding irregularities. Recent contributions have used machine learning to identify the degree of welding penetration. The basic assumption of the presented investigations is that process anomalies are the cause of welding irregularities. The focus was on detecting deviating shielding gas flow rates based on audio recordings, processed by a convolutional neural network (CNN). After adjusting the hyperparameters of the CNN it was capable of distinguishing between different flow rates of shielding gas.

Testing cockpit display systems of aircraft using a model-based approach

Avionics are highly critical systems that require extensive testing to comply with international safety standards. Cockpit display systems (CDS) are a mandatory part of modern cockpits of both manned and unmanned aircraft. The information from various avionics components is displayed on CDS using a variety of flight instruments. An important part of testing avionics systems is to evaluate whether the displayed information on the CDS is correct or not. A common industrial practice is to manually test CDS, which is time-consuming, labor-intensive, and error-prone. In this paper, we propose a model-based approach to automate the CDS testing of aircraft. The proposed approach tests the CDS at two levels: (i) at the system level to verify that the CDS are working correctly and (ii) at system integration level of CDS when these are integrated with various avionics components. As a part of our approach, we develop a UML profile to model various elements of the CDS. The models are then used to support the automated testing process. We evaluate our approach on two industrial case studies, the first case study represents a primary flight display (PFD) of an aircraft and the second one is the CDS of the ground control station (GCS-CDS) of an unmanned aerial vehicle. The evaluation results show that three potential faults are identified in the PFD and four major faults are found in the GCS-CDS.

Keyword Recorder for Robot Framework

In this era where Agile's popularity is growing fast in the IT industry, test automation becomes more and more crucial for Agile. However, test automation is time-consuming. For this reason, the student creates a tool named Keyword Recorder to create and generate test scripts — the most time-consuming part of test automation — intending to find a solution to speed up the test automation process. Keyword Recorder is a standalone program for web testing and is made to generate scripts for Robot Framework. It consists of two components: a browser, and a recorder. The browser is where the user do the web testing and the recorder’s job is to capture all the user actions along with the web elements during web testing and generate a Robot Framework’s test script based on the recorded actions.

Using a Gaussian mathematical model to define eruptive stages of young volcanic rocks in Tengchong based on laser 40Ar/39Ar dating

Reconstruction of Quaternary environments, late Cenozoic geodynamics and evaluation of volcanic hazards, all depend on the precise delineation of eruptive stages. In recent years, laser 40Ar/39Ar dating methods have been widely used for dating young volcanic rocks, given their stable automated testing process, very low background level and high sensitivity, which meet the requirements for precise dating of young samples. This paper applied high-precision laser 40Ar/39Ar dating to the main volcanic units in the Tengchong area and obtained ages in the range of 0.025–5.1 Ma using conventional data processing methods. However, conventional dating highlighted issues related to very low radiogenic 40Ar content, accidental errors and poor data stability, which led to huge age deviations. Moreover, lacking a unified timescale, conventional methods were unable to strictly define the stages of the Tengchong volcanic eruptions, leading to ongoing controversy. In this study, we applied a Gaussian mathematical model to deal with all 378 original ages from 13 samples. An apparent age-probability diagram, consisting of three independent waveforms, have been obtained. The corresponding isochron ages of these three waveforms suggest there were three volcanic eruptive stages, namely during the Pliocene (3.78±0.04 Ma), early Middle Pleistocene (0.63 ±0.03 Ma) and late Middle Pleistocene to early Late Pleistocene (0.139±0.005 Ma). These results accurately define eruptive stages in the Tengchong area.

Arrangement of the System of Network Monitoring and Assessment of the State of Information Security of an Object

Purpose of reseach is to develop a model of the system for effective collection of information about the network access object based on modern information protection methods as well as to create a kind of the reconfiguration environment in the event of a failure of computer network elements or the need to redistribute the server load. Methods. A flexible, controlled version of developing a system has been proposed for the process of collecting information about the object under study; it makes it possible to automatically identify potential vulnerabilities in the field of information security [1]. It is proposed to use the analysis of the state of an object based on the black box technique, since it makes it possible to reproduce the actions of an external intruder who does not have any information about the object at the initial stage of preparation (which is the most common scenario when adding new services) and conduct typical attacks with subsequent security evaluation [2]. A mathematical model that improves system fault-tolerance and real-time load balancing is proposed. Results. The result of the research is the development of an effective construction of a system for assessing the state of object security. A mathematical model that makes it possible to reconfigure the environment of computing modules in real time has been developed. Experimental studies confirming the completeness and correctness of the proposed solutions have been carried out. Conclusion. The proposed system is designed to analyze the compliance of the protection object with the requirements of an information security policy; it includes the stages of system analysis using the black box technique and performs the tasks of an automated testing process, system components distributions, system modules interchangeability. Together with the developed mathematical model for improving fault-tolerance and redistributing the load on computational power in the event of equipment malfunctions and bypasses in the case of complete load of the elements, the system demonstrated a decrease in the time for conducting a comprehensive assessment of the information security state of the network access object.

Impact of Channel Disturbances on Current Narrowband Power Line Communications and Lessons to Be Learnt for the Future Technologies

The electricity network is a complex communication medium with properties that depend on both the topology of the grid and the usage pattern of the connected devices. These devices generate channel disturbances during normal operation, which need to be overcome by power line communications (PLC) transmission technologies for ensuring communication. This paper analyzes the influence of the channel disturbances on the performance of the physical layer of the main narrowband PLC technologies approved by international communication organisms and currently deployed in Europe: PoweRline Intelligent Metering Evolution (PRIME) 1.3.6, PRIME 1.4 and G3-PLC. The methodology of this paper applies a standardized test method, metrics and a set of representative channel disturbances defined by the European Telecommunications Standards Institute (ETSI). Moreover, noise recordings from field measurements in an environment equipped with distributed energy resources (DER) complete the subset of the types of noise used in the study. This paper develops a replicable, fully automated, and cost optimized test scenario, based on an innovative Virtual PLC Laboratory, which provides a replicable and automated test process, where a wide range of channel disturbances can be accurately replicated, and the performance of the PLC technologies can be compared under the same conditions. The results of this paper provide important conclusions to be applied in the development of future PLC technologies.

Applying Frontier Cells Based Exploration and Lazy Theta* Path Planning over Single Grid-Based World Representation for Autonomous Inspection of Large 3D Structures with an UAS

Aerial robots are a promising platform to perform autonomous inspection of infrastructures. For this application, the world is a large and unknown space, requiring light data structures to store its representation while performing autonomous exploration and path planning for obstacle avoidance. In this paper, we combine frontier cells based exploration with the Lazy Theta* path planning algorithm over the same light sparse grid—the octree implementation of octomap. Test-driven development has been adopted for the software implementation and the subsequent automated testing process. These tests provided insight into the amount of iterations needed to generate a path with different voxel configurations. The results for synthetic and real datasets are analyzed having as baseline a regular grid with the same resolution as the maximum resolution of the octree. The number of iterations needed to find frontier cells for exploration was smaller in all cases by, at least, one order of magnitude. For the Lazy Theta* algorithm there was a reduction in the number of iterations needed to find the solution in 75% of the cases. These reductions can be explained both by the existent grouping of regions with the same status and by the ability to confine inspection to the known voxels of the octree.

SOFTWARE DEFECT PREDICTION USING DEEP BELIEF NETWORK WITH L1-REGULARIZATION BASED OPTIMIZATION

Abstract: Software defect prediction is considered as most interesting area for researchers in the field of software engineering. This process of defect prediction, identifies the bug during automated testing process which prevents the development of faulty software module. According to this process, previous archives of software modules are considered for analyzing the quality based on data mining and machine leaning concepts, which identifies the faults in software modules. Several techniques have been presented for software defect prediction using data mining techniques and machine learning techniques but achieving desired accuracy in performance is a challenging task for researchers. To address this issue, in this work we have presented a new approach for software defect prediction by combining genetic algorithm optimization process for feature subspace reduction and deep belief network for pattern learning. Deep belief networks are further improved by applying L1-regularization scheme resulting in better learning process by reducing the overfitting error. This combined model is implemented on SPIE lab software defect database. An extensive experimental study is carried out which shows that proposed approach achieves higher accuracy when compared with state-of-the-art software defect prediction techniques.

Evaluation of Software Testing Techniques Using Artificial Neural Network

Software Industry plays a vital role in the current environment, it is very essential to minimize the fault in the existing software products. In SDLC life cycle, software testing becomes very much important which finds faults in the software that increases the efficiency of software. Most of the cost is occupied by the software testing process, it is product very essential to implement the automation technique that reduces the cost that increases the software reliability. In the automated testing process, various intelligent methods are used in order to avoid the manual process. Automation testing tools can be implemented to increase the quality of the software.

Lifecycle, Iteration, and Process Automation with SMS Gateway

Producing a better quality software system requires an understanding of the indicators of the software quality through defect detection, and automated testing. This paper aims to elevate the design and automated testing process in an engine water pump of a drinking water plant. This paper proposes how software developers can improve the maintainability and reliability of automated testing system and report the abnormal state when an error occurs on the machine. The method in this paper uses literature to explain best practices and case studies of a drinking water plant. Furthermore, this paper is expected to be able to provide insights into the efforts to better handle errors and perform automated testing and monitoring on a machine.

A lightweight robot system designed for the optimisation of an automotive end-off line process station

Purpose – This study aims to deliver an approach of how lightweight robot systems can be used to automate manual processes for higher efficiency, increased process capability and enhanced ergonomics. As a use case, a new collaborative testing system for an automated water leak test was designed using an image processing system utilized by the robot. Design/methodology/approach – The “water leak test” in an automotive final assembly line is often a significant cost factor due to its labour-intensive nature. This is particularly the case for premium car manufacturers as each vehicle is watered and manually inspected for leakage. This paper delivers an approach that optimizes the efficiency and capability of the test process by using a new automated in-line inspection system whereby thermographic images are taken by a lightweight robot system and then processed to locate the leak. Such optimization allows the collaboration of robots and manual labour, which in turn enhances the capability of the process station. Findings – This paper examines the development of a new application for lightweight robotic systems and provides a suitable process whereby the system was optimized regarding technical, ergonomic and safety-related aspects. Research limitations/implications – A new automated testing process in combination with a processing algorithm was developed. A modular system suitable for the integration of human–robot collaboration into the assembly line is presented as well. Practical implications – To optimize and validate the system, it was set up in a true to reality model factory and brought to a prototypical status. Several original equipment manufacturers showed great interest in the system. Feasibility studies for a practical implementation are running at the moment. Social implications – The direct human–robot collaboration allows humans and robots to share the same workspace without strict separation measures, which is a great advantage compared with traditional industrial robots. The workers benefit from a more ergonomic workflow and are relieved from unpleasant, repetitive and burdensome tasks. Originality/value – A lightweight robotic system was implemented in a continuous assembly line as a new area of application for these systems. The automated water leak test gives a practical example of how to enrich the assembly and commissioning lines, which are currently dominated by manual labour, with new technologies. This is necessary to reach a higher efficiency and process capability while maintaining a higher flexibility potential than fully automated systems.

Automated information-measurement system used to test the immunity of the medical equipment protective elements to the discharges of the defibrillator

The article discusses the information-measurement system used for testing of protective elements medical equipment for their resistance to the discharge of a defibrillator, which is designed to test medical products, which may be exposed to high voltage defibrillator pulses, up to 5000V while in operation. The Information-measurement system should provide a fully automated testing process in accordance with the international standard IEC 60601-1, as well as analyze the result and diagnose the parameters of elements the of a defibrillator block simulating the pulses.

Inspector Robot – A new collaborative testing system designed for the automotive final assembly line

Purpose – The paper aims to deliver an approach of how lightweight robot systems can be used to automate manual processes for higher efficiency, increased process capability and enhanced ergonomics. To show how these systems can be utilized in practice, a new collaborative testing system for an automated water leak test was designed using an image processing system utilized by the robot. Design/methodology/approach – The “water leak test” in an automotive final assembly line is often a significant cost factor due to its labour-intensive nature. This is particularly the case for premium car manufacturers as each vehicle is watered and manually inspected for leakage. This paper delivers an approach that optimizes the efficiency and capability of the test process by using a new automated in-line inspection system whereby thermographic images are taken by a lightweight robot system and then processed to locate the leak. Such optimization allows the collaboration of robots and manual labour which, in turn, enhances the capability of the process station. Findings – This paper examines the development of novel applications for lightweight robotic systems and provides a suitable process whereby the systems are optimized in technical, ergonomic and safety-related aspects. Research limitations/implications – A new automated testing process in combination with a processing algorithm was developed. Practical implications – To optimize and validate the system, it was set up in a true to reality model factory and brought to a prototypical status. Several original equipment manufacturers showed great interest in implementing the system in their assembly line. Social implications – The direct human–robot collaboration allows humans and robots to share the same workspace without strict separation measures which is a great advantage compared with traditional industrial robots. The workers benefit from a more ergonomic workflow and are relieved from unpleasant, repetitive and burdensome tasks. Originality/value – A lightweight robotic system was implemented in a continuous assembly line as a new area of application for these systems. The automated water leak test gives a practical example of how to enrich the assembly and commissioning lines, which are currently dominated by manual labour, with new technologies. This is necessary to reach a higher efficiency and process capability while maintaining a higher flexibility potential than fully automated systems.

Research on Automated Testing Process Model for UOF/OOXML Translator

There are some issues in the manual test of UOF/OOXML Translator, such as the low efficiency, difficult measurement of test case coverage and project delay. This paper brings about the automated testing process model based on the manual V model of UOF/OOXML Translator, and proposes the implementation scheme of automated testing process through analyzing test case automatic generation, test case management, automatic contrast of function points and defects management. The experiments have shown that this model can effectively manage test cases and defects, and even replace manual testing partly. Besides, this model can improve testing efficiency and reduce test workload, preventing the defects occurring in the late project stage.

Curvature Characterization of Flex Sensors for Human Posture Recognition

Resistive flex sensors have been increasingly used in different areas for their interesting property to change their resistance when bent. They can be employed in those systems where a joint rotation has to be measured, in particular biomedical systems, to measure human joint static and dynamic postures. In spite of their interesting properties, such as robustness, low price and long life, to date commercial flex sensors have been only characterized against the variation of the bend angle with small fixed curvature radius mostly around the device center, so limiting the application to the measure of human joints. Here we aimed to investigate the flex sensor's response when there is a change in curvature radius as it is, for instance, for the measure of the postures of the human torso. So, we designed a novel automated test process to obtain resistances vs. curvature radius pairs. Results demonstrated the usability of flex sensors to other parts of the human body rather than simply joints, differently as it is currently done.

A method of minimum reusability estimation for automated software testing

Through reusing software test components, automated software testing generally costs less than manual software testing. There has been much research on how to develop the reusable test components, but few fall on how to estimate the reusability of test components for automated testing. The purpose of this paper is to present a method of minimum reusability estimation for automated testing based on the return on investment (ROI) model. Minimum reusability is a benchmark for the whole automated testing process. If the reusability in one test execution is less than the minimum reusability, some new strategies must be adopted in the next test execution to increase the reusability. Only by this way, we can reduce unnecessary costs and finally get a return on the investment of automated testing.