Software Testing Framework Research Articles

Software Testing Framework for the Financial Market

A well designed, developed and tested software is usually reliable and it produces the same consistent outputs for a set of inputs. However, financial markets software is different because it can produce different results for the same periods of back-testing with the same input historical data, usually downloaded from the financial market broker’s trading server. These inconsistency of results can confuse a financial market software developer when testing for the profitability of developed expert advisors because a profitable expert advisor can be wrongly discarded as unprofitable, leading to frustrations. This problem can be addressed when new software testing processes and indicators are added to the conventional ones such as functional testing, performance testing, usability testing, etc., associated with normal software development. This paper proposes a software testing framework for the financial market with novel software testing processes and indicators. The proposed software testing framework integrates six software testing processes namely, brokers test, currency pairs test, spread test, weekday-weekend test, back testing-live test and time and space overhead test. The paper further analyzes the problem of time and space overheads associated with the financial market software during back-testing and real life implementation. The framework was applied to real life trading in the Forex financial market. The results show that the proposed framework improves the profitability of the financial market software when applied in different scenarios.

Software Testing of eHealth Interventions: Existing Practices and the Future of an Iterative Strategy.

eHealth interventions are becoming a part of standard care, with software solutions increasingly created for patients and health care providers. Testing of eHealth software is important to ensure that the software realizes its goals. Software testing, which is comprised of alpha and beta testing, is critical to establish the effectiveness and usability of the software. In this viewpoint, we explore existing practices for testing software in health care settings. We scanned the literature using search terms related to eHealth software testing (eg, "health alpha testing," "eHealth testing," and "health app usability") to identify practices for testing eHealth software. We could not identify a single standard framework for software testing in health care settings; some articles reported frameworks, while others reported none. In addition, some authors misidentified alpha testing as beta testing and vice versa. There were several different objectives (ie, testing for safety, reliability, or usability) and methods of testing (eg, questionnaires, interviews) reported. Implementation of an iterative strategy in testing can introduce flexible and rapid changes when developing eHealth software. Further investigation into the best approach for software testing in health care settings would aid the development of effective and useful eHealth software, particularly for novice eHealth software developers.

V-MODEL FOR AIR DEFENSE SYSTEMS

This article explores the application of the V model, a structured software development and testing framework, to air defense systems. The V model's sequential approach aligns development and testing phases, promoting comprehensive verification and validation. The article delineates the adaptation of the V model to air defense systems, encompassing both verification and validation activities. Verification ensures adherence to design and requirements, while validation assesses system alignment with user needs. The article underscores the benefits of early issue identification, higher-quality software, and improved system performance through the V model's systematic approach.

Testing machine learning systems in real estate

AbstractUncertainty about the inner workings of machine learning (ML) models holds back the application of ML‐enabled systems in real estate markets. How do ML models arrive at their estimates? Given the lack of model transparency, how can practitioners guarantee that ML systems do not run afoul of the law? This article first advocates a dedicated software testing framework for applied ML systems, as commonly found in computer science. Second, it demonstrates how system testing can verify that applied ML models indeed perform as intended. Two system‐testing procedures developed for ML image classifiers used in automated valuation models (AVMs) illustrate the approach.

Hybrid support vector machine and K‐nearest neighbor‐based software testing for educational assistant

AbstractIn terms of training students for work in diverse firms, traditional and out‐of‐date teaching techniques cannot compete with digital teaching methods. To overcome this problem, the teaching approach and content must be changed. An Educational Assistant for Software Testing (EAST) framework is developed in this work to train students to improve their skills in software testing via Computer Assisted Instruction (CAI) built using Natural Language Processing (NLP), Machine learning, and information retrieval techniques. In this paper, a Group Search Optimized two‐stage hybrid Support Vector Machine‐K‐Nearest Neighbor (SVM‐KNN) classifier is used to develop a novel approach for analyzing the parameters that introduce bugs in bug reports. To decrease the data sparsity problem, the group search optimization (GSO) algorithm is used to improve the parameter selection process of the two‐stage hybrid classifier by generating optimal values for parameters such as k, c, and gamma. Two bug report datasets were used to test the model. The database for our application is built by collecting bug reports from a wide open‐source community as well as several mobile application development companies. Based on the extensive experiments conducted via different performance metrics, we can conclude that the EAST framework can improve outdated teaching methodologies.

Software testing framework for ERP systems based on agile development

Software testing framework for ERP systems based on agile development

An Automated Testing Framework for Gesture Recognition System using Dynamic Image Pattern Generation with Augmentation

In the field of information technology, the gesture recognition system plays a very essential role. As it has achieved vast importance, it is mandatory to test the recognition system to ensure the quality of the system by identifying the bugs in the software. In our research, we suggested a dynamic testing method for gesture recognition software. using dynamic image pattern generation with augmentation. The automated software testing framework is a set of processes to create new test cases for properly testing a image processing software. The research intention for generate automated testing cases by following a standard process which helps to increase the performance and efficiency of the gesture recognition system. We have built the framework to give proper testing and give result (accuracy and defect) for which gesture recognition system already in the market. our research, the team strongly following and adding two software testing standard. First one is ISO/IEC/IEEE/291129-3 to define the process for testing software. And the second one is ISO/IEC/IEEE/291129-5 to implement the techniques for software testing. We proposed this framework with major five parameters by noise, rotation, background, contrast, and scale. Which are the most use with every gesture recognition system. Our developed framework’s phase is used to generate new testing cases based on the existing gesture recognition system’s data. There are we work with five systems, commonly with the gesture recognition for experiments. We provide the testing report with total accuracy and defect by comparing existing well-known system’s data. At the final result, our system suggested an analysis report based on the testing result. And tell what are the improvement needs for the existing system to consider noised images or different scaled images to build a robust system.
 GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 42-50

An Intelligent Mobile Application Testing Experience Report

Artificial intelligence applications provide tremendous opportunities to improve human life and drive innovation. AI systems/applications which operate in a real-world environment have to encounter an infinite set of feasible scenarios. Conventional testing approach to test the AI application allows only limited testing and does not allow taking the different contexts into consideration and may lead to insufficient validation and characterization. Therefore, to ensure robustness, certainty and reliability of AI applications, the authors applied classification-based AI software testing framework and 3D decision tables to generate test cases. Moreover, the authors compared the quality assurance metrics (accuracy, correctness, reliability and consistency) of AI and non-AI functions in the AI mobile application scenario. Our results indicate and confirm that complete AI function validation is not possible with conventional testing methods, but AI software testing strategy proposed based on classification framework and 3D decision tables has a good effect.

Towards Accountability in Machine Learning Applications: A System-testing Approach

A rapidly expanding universe of technology-focused startups is trying to change and improve the way real estate markets operate. The undisputed predictive power of machine learning (ML) models often plays a crucial role in the 'disruption' of traditional processes. However, an accountability gap prevails: How do the models arrive at their predictions? Do they do what we hope they do – or are corners cut? Training ML models is a software development process at heart. We suggest following the dedicated software testing framework and verifying that the ML model is performing as intended. Illustratively, we augment two image classifiers with a system testing procedure based on local interpretable model-agnostic explanation (LIME) techniques. Analyzing the classifications sheds light on some of the factors that determine the behavior of the systems. We show that cross-validation is simply not good enough when operating in regulated environments.

RETRACTED: Development of smart english classroom system based on FPGA software and hardware co-simulation test

Improving English proficiency for professional students is a significant challenge in the AEC and the Renaissance. The problem found in the Polytechnic Gland Shipping Research Institute is the lack of time motivation and insufficient time for students to study outside the classroom lack of time. Therefore developed an Android-based “Technical English” application that supports PPN, English teaching, and learning processes. Development and Smart Classroom List (SCI) Verification. SCI is a learning environment tool that integrates existing technologies, including TROFLEI, TICI and CCEI (ergonomics). SCI is included to learn the functions, materials to describe flexibility, and experience factors in the smart classroom. The information technology detail used in some projects has also been adapted to the elegant classroom environment. Older classmates check the tool and reveal ten balances: body design, flexibility, and use of technology, learning data diversity, inquiry, collaboration, student drive, honesty and learning experience. The results of the Acid Ceasefire Agreement prove that is a simple tool for assessing the learning environment in a smart classroom. Student Differences, Flexibility, Technology Application, and Learning Data Factors There are four significant practical priority differences in the learning environment in a smart classroom. FPGA has received a lot of attention from the nuclear industry as an alternative platform for logical programming controllers for digitalization and control. The FPGA development software feature includes several steps for integration and refinement, and each such step requires a different simulation verification step. This research proposes an integrated software testing framework that simulates all the glitches created by FPGA software and evaluates whether it can work in general, using the standard Oracle programs for all glitches simultaneously.

A Pattern-Based Software Testing Framework for Exploitability Evaluation of Metadata Corruption Vulnerabilities

In recent years, increased attention is being given to software quality assurance and protection. With considerable verification and protection schemes proposed and deployed, today’s software unfortunately still fails to be protected from cyberattacks, especially in the presence of insecure organization of heap metadata. In this paper, we aim to explore whether heap metadata could be corrupted and exploited by cyberattackers, in an attempt to assess the exploitability of vulnerabilities and ensure software quality. To this end, we propose RELAY, a software testing framework to simulate human exploitation behavior for metadata corruption at the machine level. RELAY employs the heap layout serialization method to construct exploit patterns from human expertise and decomposes complex exploit-solving problems into a series of intermediate state-solving subproblems. With the heap layout procedural method, RELAY makes use of the fewer resources consumed to solve a layout problem according to the exploit pattern, activates the intermediate state, and generates the final exploit. Additionally, RELAY can be easily extended and can continuously assimilate human knowledge to enhance its ability for exploitability evaluation. Using 20 CTF&RHG programs, we then demonstrate that RELAY has the ability to evaluate the exploitability of metadata corruption vulnerabilities and works more efficiently compared with other state-of-the-art automated tools.

FMRIPrep: a robust preprocessing pipeline for functional MRI.

Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad-hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than commonly used preprocessing tools. FMRIPrep equips neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of their results.

Enhancing Software Testing Framework in Public Cloud

Enhancing Software Testing Framework in Public Cloud

Input-Domain Software Testing for Failure Probability Estimation of Safety-Critical Applications in Consideration of Past Input Sequence

Software failure probability quantification is an important aspect of digital system reliability assessment. Several quantification methods currently available in the software reliability field have characteristics unsuitable for application to safety-critical software. In this paper, a software test framework in consideration of input trajectory is developed, and a software failure probability quantification method is also suggested. The test input cases consist of the states and present inputs, where input trajectory is represented by the state. To obtain the input domain, which represents realistic plant behavior, digital system characteristics and plant dynamics are considered. This allows software failure probability to be estimated by using the result of each representative test case, thus reducing testing efforts. The proposed framework was applied to a nuclear power plant reactor protection system as an example to show its effectiveness. The method provides a practical and relatively simple way to test software and estimate software failure probability.

Generalised framework for optimal pre and post release software testing in presence of warranty

Generalised framework for optimal pre and post release software testing in presence of warranty

Generalised framework for optimal pre and post release software testing in presence of warranty

Significant advances in the field of software testing have been made by providing extended warranties, operational support and desired software reliability to the customers. However, the trade-off between minimising testing cost and maximising reliability to ensure well-timed market penetration has been a key concern for developers. Separate release and testing stop time strategy in order to optimise overall testing and market opportunity cost is proposed. We propose a generalised framework for developers to achieve multiple objectives of minimum testing and market opportunity cost; optimal warranty length; and optimal testing and release time. Software reliability growth models (SRGMs) are used to model an average number of bugs detected by testers (users) during the pre (post) release of software. Significant implications and quite intriguing facts related to separate release and testing stop time; early release ensuring higher reliability and; capitalising market opportunity to minimise overall testing cost are suggested based on proposed optimisation models. Numerical illustration is provided to validate the proposed framework.

Optimized Order of Software Testing Techniques in Agile Process – A Systematic Approach

The designing, development of a software product needs lot of efforts whereas software testing is also a very challenging task but it is equally mandatory activity in order to ensure the quality of the product before shipping to customer. When it comes to the Agile model under which software builds are developed very frequently and development goes on a very high pace, software testing becomes more important and critical. Organizations following the agile methodology, encounter number of problems in formulating a software testing process unless they come up with a systematic testing approach based on right testing technique at a proper stage of the agile process. This paper addresses the relevant software testing techniques feasible at different stages of the agile process and proposes a dedicated software testing framework producing quality software products developed under agile methodology.

An Integrated Software Testing Framework for FPGA-Based Controllers in Nuclear Power Plants

Field-programmable gate arrays (FPGAs) have received much attention from the nuclear industry as an alternative platform to programmable logic controllers for digital instrumentation and control. The software aspect of FPGA development consists of several steps of synthesis and refinement, and also requires verification activities, such as simulations that are performed individually at each step. This study proposed an integrated softwaretesting framework for simulating all artifacts of the FPGA software development simultaneously and evaluating whether all artifacts work correctly using common oracle programs. This method also generates a massive number of meaningful simulation scenarios that reflect reactor shutdown logics. The experiment, which was performed on two FPGA software implementations, showed that it can dramatically save both time and costs.

Canonical, stable, general mapping using context schemes.

Sequence mapping is the cornerstone of modern genomics. However, most existing sequence mapping algorithms are insufficiently general. We introduce context schemes: a method that allows the unambiguous recognition of a reference base in a query sequence by testing the query for substrings from an algorithmically defined set. Context schemes only map when there is a unique best mapping, and define this criterion uniformly for all reference bases. Mappings under context schemes can also be made stable, so that extension of the query string (e.g. by increasing read length) will not alter the mapping of previously mapped positions. Context schemes are general in several senses. They natively support the detection of arbitrary complex, novel rearrangements relative to the reference. They can scale over orders of magnitude in query sequence length. Finally, they are trivially extensible to more complex reference structures, such as graphs, that incorporate additional variation. We demonstrate empirically the existence of high-performance context schemes, and present efficient context scheme mapping algorithms. The software test framework created for this study is available from https://registry.hub.docker.com/u/adamnovak/sequence-graphs/. anovak@soe.ucsc.edu Supplementary data are available at Bioinformatics online.

CRITICAL COMPONENTS IDENTIFICATION USING MUTATION BASED COMPONENTS IMPACT ANALYSIS

High quality software can be obtained by means of resolving the complexity of the software. According to Pareto principle, 20% of components lead to 80% of the problems [1]. So, we need to identify those 20% of the components during testing. Therefore, this research work suggested an automated software testing framework to identify critical components using mutant based dynamic impact analysis for Software under Test (SUT). Mutants are automatically generated by injecting faults in the components using Offutt mutation operators and they are utilised to identify their impact level over other components of the system. The generated mutants and original program are executed using the suite of test cases, based on the conclusion of both the results, the mutation score is assessed and furthermore it is utilised as the test case adequacy criterion to recognize the impact level of it over the other components of a system. The outcome of this innovative approach is a testing tool entitled as JImpact Analyzer that automates the entire task and has generates miscellaneous graphs for visualization purpose.