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As software projects evolve and grow in size and complexity, so do their test suites. Test suite reduction (TSR) aims at reducing the size of a test suite by removing redundant and obsolete test cases based on a coverage metric while preserving its fault detection capabilities. The contributions of this paper are twofold: (1) We examine a lesser‐known coverage criterion, that is, checked coverage. Checked coverage not only investigates if a part of the code was executed but also if it was checked by a test oracle. In an empirical evaluation, we performed TSR based on different reduction algorithms, coverage metrics, and open‐source Java projects with our own TSR tool to determine the most effective and efficient combination of metric and method. (2) Given the results of the first evaluation, we further investigate the potential of parameter optimization in regard to a genetic reduction algorithm. In particular, we focus on finding a general setting for the parameters crossover rate and mutation rate such that test suites can be reduced in a reasonable time while maintaining a high fault detection power. [ABSTRACT FROM AUTHOR]

The three‐body recombination reaction, or ternary association, is a termolecular reaction leading to a molecule after a three‐body encounter that plays a vital role in many relevant scenarios in chemical physics. Here, we introduce the Python 3‐Body Recombination program, which is dedicated to the computation of atomic three‐body recombination rate coefficients. The software is based on a classical trajectory approach in hyperspherical coordinates after mapping the three‐body problem as a single particle in a higher‐dimensional space. This theoretical approach is fully general and applicable to any ion‐atom‐atom or atom‐atom‐atom three‐body process. The predictive power of the methodology has been tested in several different experimental scenarios, reaching a good description of every system. The code structure is presented alongside examples and tests to ensure the software's capacity. In addition, the performance of the software after parallelization is shown. [ABSTRACT FROM AUTHOR]

Software defects are a critical issue in software development that can lead to system failures and cause significant financial losses. Predicting software defects is a vital aspect of ensuring software quality. This can significantly impact both saving time and reducing the overall cost of software testing. During the software defect prediction (SDP) process, automated tools attempt to predict defects in the source codes based on software metrics. Several SDP models have been proposed to identify and prevent defects before they occur. In recent years, recurrent neural network (RNN) techniques have gained attention for their ability to handle sequential data and learn complex patterns. Still, these techniques are not always suitable for predicting software defects due to the problem of imbalanced data. To deal with this problem, this study aims to combine a bidirectional long short-term memory (Bi-LSTM) network with oversampling techniques. To establish the effectiveness and efficiency of the proposed model, the experiments have been conducted on benchmark datasets obtained from the PROMISE repository. The experimental results have been compared and evaluated in terms of accuracy, precision, recall, f-measure, Matthew's correlation coefficient (MCC), the area under the ROC curve (AUC), the area under the precision-recall curve (AUCPR) and mean square error (MSE). The average accuracy of the proposed model on the original and balanced datasets (using random oversampling and SMOTE) was 88%, 94%, And 92%, respectively. The results showed that the proposed Bi-LSTM on the balanced datasets (using random oversampling and SMOTE) improves the average accuracy by 6 and 4% compared to the original datasets. The average F-measure of the proposed model on the original and balanced datasets (using random oversampling and SMOTE) were 51%, 94%, And 92%, respectively. The results showed that the proposed Bi-LSTM on the balanced datasets (using random oversampling and SMOTE) improves the average F-measure by 43 and 41% compared to the original datasets. The experimental results demonstrated that combining the Bi-LSTM network with oversampling techniques positively affects defect prediction performance in datasets with imbalanced class distributions. [ABSTRACT FROM AUTHOR]

PPP_Mansoura is a new software that can process multi-GNSS data work on MATLAB environment and linked with C# in the preprocessing stage. It gives highly accurate results and provides a results file for each epoch, and the users can choose the GNSS system they want to run with the primary systems (GPS or GLONASS) and all this with simple MATLAB Code. For testing the software, we processed the raw data (RINEX 3) from 17 MGEX stations for 24 h data during 1-week with a 30-s interval time and submitted it to the new software and PPPH software. The averaged positioning errors obtained from PPP_Mansoura and PPPH were 5.14 mm and 6.9 mm respectively, for the East direction, 11.6 mm and 14 mm for the North direction, and 14.56 mm and 20.4 mm respectively for the Up direction, the averaged convergence time obtained from PPP_Mansoura and PPPH were 35.3 min and 54.47 min, so the results show that PPP_Mansoura give results with high accuracy can be comparable with PPP standards results and PPP software results. [ABSTRACT FROM AUTHOR]

The efficiency and performance of a software application largely depend on the testing strategy adopted by the firm. Apart from the tools, techniques, and skills used for testing, the duration also plays an important influence in establishing software reliability. This defines the operational performance of the software. The testing duration decision is dependent on the failure behavior depicted by the software during the process of testing and the cost spent at various phases of development. In this paper, we study the multi-release model whose fault removal process is affected by random irregular fluctuations (white noise) and error generation phenomenon and determine the optimal time of testing for a multi-release-based software. The fault removal process is governed by the phenomenon of testing coverage which is affected by random fluctuations. The model shows encouraging results as it handles the stochastic property of the fault detection process. The optimal testing time is determined with the goal to minimize the expected development cost related to software while achieving the desired reliability levels of software for that release using a Genetic Algorithm. A real-life four-release fault dataset of tandem computers has been used to numerically demonstrate the methodology. It is observed through sensitivity analysis that the presence of white noise directly affects the cost and optimal testing duration. The potential to improve sensitivity, flexibility, early detection, discovering unsuspected patterns, and boost fault diagnosis is enhanced by collecting irregular fluctuations in the fault detection rate. This technology, by going beyond existing methodologies, has distinct advantages for detecting faults and can contribute to more dependable and efficient systems in a variety of domains. [ABSTRACT FROM AUTHOR]

Testing and debugging have been the most significant steps of software development since it is tricky for engineers to create error-free software. Software testing takes place after coding with the goal of finding flaws. If errors are found, debugging would be done to identify the source of the errors so that they may be fixed. Detecting as well as locating defects are thus two essential stages in the creation of software. We have created a unique approach with the following two working phases to generate a minimized test suite that is capable of both detecting and localizing faults. In the initial test suite minimization process, the cases were generated and minimized based on the objectives such as D-score and coverage by the utilization of the proposed Blue Monkey Customized Black Widow (BMCBW) algorithm. After this test suite minimization, the fault validation is done which includes the process of fault detection and localization. For this fault validation, we have utilized an improved Long Short-Term Memory (LSTM). At 90% of the learning rate the accuracy of the presented work is 0.97%, 2.20%, 2.52%, 0.97% and 2.81% is better than the other extant models like AOA, COOT, BES, BMO and BWO methods. The results obtained proved that our Blue Monkey Customized Black Widow Optimization-based fault detection and localization approach can provide superior outcomes. [ABSTRACT FROM AUTHOR]

With the constant growth of software-defined radio (SDR) technologies in fields related to wireless communications, the need for efficient ways of testing and evaluating the physical-layer (PHY) protocols developed for these technologies in real-life traffic scenarios has become more critical. This paper proposes a software testbed that enhances the creation of network environments that allow GNU radio applications to be fed with test traffic in a simple way and through an interoperable interface. This makes the use of any traffic generator possible—existing ones or one that is custom-built—to evaluate a GNU radio application. In addition, this paper proposes an efficient way to collect PHY-specific monitoring data to improve the performance of the critical components of the message delivery path by employing the protocol buffers library. This study considers the entire testing and evaluation ecosystem and demonstrates how PHY-specific monitoring information is collected, handled, stored, and processed as time series to allow complex visualization and real-time monitoring. [ABSTRACT FROM AUTHOR]

The cost of software testing could be reduced if faulty entities were identified prior to the testing phase, which is possible with software fault prediction (SFP). In most SFP models, machine learning (ML) methods are used, and one aspect of improving prediction accuracy with these methods is tuning their control parameters. However, parameter tuning has not been addressed properly in the field of software analytics, and the conventional methods (such as basic Differential Evolution, Random Search, and Grid Search) are either not up-to-date, or suffer from shortcomings, such as the inability to benefit from prior experience, or are overly expensive. This study aims to examine and propose parameter tuners, called DEPTs, based on different variants of Differential Evolution for SFP with the Swift-Finalize strategy (to reduce runtime), which in addition to being up-to-date, have overcome many of the challenges associated with common methods. An experimental framework was developed to compare DEPTs with three widely used parameter tuners, applied to four common data miners, on 10 open-source projects, and to evaluate the performance of DEPTs, we used eight performance measures. According to our results, the three tuners out of five DEPTs improved prediction accuracy in more than 70% of tuned cases, and occasionally, they exceeded benchmark methods by over 10% in case of G-measure. The DEPTs took reasonable amounts of time to tune parameters for SFP as well. [ABSTRACT FROM AUTHOR]