This paper presents a retrospective of the article "Whole Test Suite Generation", published in the IEEE Transactions on software Engineering, in 2012. We summarize its main contributions, and discuss how thi...
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This paper presents a retrospective of the article "Whole Test Suite Generation", published in the IEEE Transactions on software Engineering, in 2012. We summarize its main contributions, and discuss how this work impacted the research field of Search-Based software Testing (SBST) in the last 12 years. The novel techniques presented in the paper were implemented in the tool EvoSuite, which has been so far the state-of-the-art in unit test generation for Java programs using SBST. SBST has shown practical and impactful applications, creating the foundations to open the doors to tackle several other software testing problems besides unit testing, like for example system testing of Web APIs with EvoMaster. We conclude our retrospective with our reflections on what lies ahead, especially considering the important role that SBST still plays even in the age of Large Language Models (LLMs).
Modern high-dimensional datasets are often formed by acquiring samples from multiple sources having heterogeneous quality, i.e., some sources are noisier than others. Collecting data in this manner raises the followin...
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Modern high-dimensional datasets are often formed by acquiring samples from multiple sources having heterogeneous quality, i.e., some sources are noisier than others. Collecting data in this manner raises the following natural question: what is the best way to collect the data (i.e., how many samples should be acquired from each source) given constraints (e.g., on time or energy)? In general, the answer depends on what analysis is to be performed. In this paper, we study the foundational signal processing task of estimating underlying low-dimensional principal components. Since the resulting dataset will be high-dimensional and will have heteroscedastic noise, we focus on the recently proposed optimally weighted PCA, which is designed specifically for this setting. We develop an efficient method for designing sample acquisitions that optimize the asymptotic performance of optimally weighted PCA given resource constraints, and we illustrate the proposed method through various case studies.
Modem softwarization, where baseband signals are fully processed using software on a general-purpose CPU, is a promising technology in mobile communications due to its simplicity and flexibility in realizing various f...
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Modem softwarization, where baseband signals are fully processed using software on a general-purpose CPU, is a promising technology in mobile communications due to its simplicity and flexibility in realizing various features. On the other hand, many still question the effectiveness of a softwarized modem in commercial environments concerning performance and complexity. Motivated by this perspective, this paper presents the design and implementation of a softwarized modem with the specific feature of 5G cell search for field evaluation. Based on the baseline algorithms of 5G cell search in Open Air Interface (OAI), we propose a new software architecture which can efficiently manage a 5G cell search procedure and decompose the overall 5G cell search into sub-algorithms. We also design and implement novel sub-algorithms that enhance the detection of Synchronization Signal Blocks (SSBs). Our softwarized modem utilizes dual-rate sampling to significantly reduce computation complexity during timing offset estimation. It also adaptively detects synchronization signals or cell identities based on the presence of inter-cell interference or multi-path fading. The performance evaluation through field experiments concludes that our softwarized modem outperforms the baseline, and the proposed sub-algorithms are effective in enhancing cell search performance. The detection probability and time consumption results for our softwarized modem confirm that it is feasible for commercial uses.
Effort estimation is the most critical activity for the success of overall solution delivery in software engineering projects. In this context, the paper's main contributions to the literature on software effort e...
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Effort estimation is the most critical activity for the success of overall solution delivery in software engineering projects. In this context, the paper's main contributions to the literature on software effort estimation are twofold. First, this paper examines the application of meta-heuristic algorithms to have a logical and acceptable parametric model for software effort estimation. Secondly, to unravel the benefits of nature-inspired meta-heuristic algorithms usage in optimizing Deep Learning (DL) architectures for software effort estimation, this paper presents a Deep Neural Network (DNN) model for software effort estimation based on meta-heuristic algorithms. In this paper, Grey Wolf Optimizer (GWO) and StrawBerry (SB) meta-heuristic algorithms are applied for having a logical and acceptable parametric model for software effort estimation. To validate the performances of these two algorithms, a set of nine benchmark functions having wide dimensions is applied. Results from GWO and SB algorithms are compared with five other meta-heuristic algorithms used in literature for software effort estimation. Experimental results showed that the GWO has comprehensive superiority in terms of accuracy in estimation. The proposed DNN model (GWDNNSB) using meta-heuristic algorithms for initial weights and learning rate selection, produced better results compared to existing work on using DNN for software effort estimation.
AC-3 audio coding technology is a kind of Perceptual Audio Coder (PAC) developed by the Dolby Company. Up to 5 full-bandwidth channels and one subwoofer channel (cutoff at 120Hz) are available in AC-3 to provide multi...
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AC-3 audio coding technology is a kind of Perceptual Audio Coder (PAC) developed by the Dolby Company. Up to 5 full-bandwidth channels and one subwoofer channel (cutoff at 120Hz) are available in AC-3 to provide multi-channel, low bit rate, and high perceptual quality of audio. This explains why AC-3 has become the audio standard of many international standards. In this paper, we focus on the real-time software implementation issues of AC-3, Two fast algorithms of the time-frequency transform, one for memory economization and the other is for time domain subsampling, are presented. Meanwhile, we will state the current performance status of our AC-3 decoder.
This paper integrates an estimation of distribution (EoD)-based update operator into decomposition-based multiobjective evolutionary algorithms for binary optimization. The probabilistic model in the update operator i...
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This paper integrates an estimation of distribution (EoD)-based update operator into decomposition-based multiobjective evolutionary algorithms for binary optimization. The probabilistic model in the update operator is a probability vector, which is adaptively learned from historical information of each subproblem. We show that this update operator can significantly enhance decomposition-based algorithms on a number of benchmark problems. Moreover, we apply the enhanced algorithms to the constrained optimal software product selection (OSPS) problem in the field of search-based software engineering. For this real-world problem, we give its formal definition and then develop a new repair operator based on satisfiability solvers. It is demonstrated by the experimental results that the algorithms equipped with the EoD operator are effective in dealing with this practical problem, particularly for large-scale instances. The interdisciplinary studies in this paper provide a new real-world application scenario for constrained multiobjective binary optimizers and also offer valuable techniques for software engineers in handling the OSPS problem.
The discovery of software faults at early stages plays an important role in improving software quality;reduce the costs, time, and effort that should be spent on software development. Machine learning (ML) have been w...
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The discovery of software faults at early stages plays an important role in improving software quality;reduce the costs, time, and effort that should be spent on software development. Machine learning (ML) have been widely used in the software faults prediction (SFP), ML algorithms provide varying results in terms of predicting software fault. Deep learning achieves remarkable performance in various areas such as computer vision, natural language processing, speech recognition, and other fields. In this study, two deep learning algorithms are studied, Multi-layer perceptron & x2019;s (MLPs) and Convolutional Neural Network (CNN) to address the factors that might have an influence on the performance of both algorithms. The experiment results show how modifying parameters is directly affecting the resulting improvement, these parameters are manipulated until the optimal number for each of them is reached. Moreover, the experiments show that the effect of modifying parameters had an important role in prediction performance, which reached a high rate in comparison with the traditional ML algorithm. To validate our assumptions, the experiments are conducted on four common NASA datasets. The result shows how the addressed factors might increase or decrease the fault detection rate measurement. The improvement rate was as follows up to 43.5 & x0025;for PC1, 8 & x0025;for KC1, 18 & x0025;for KC2 and 76.5 & x0025;for CM1.
An interactive software is presented for evaluating algorithms for digital relay designs. The software includes signal processing and protection modules that are used in digital relays. The software also includes faci...
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An interactive software is presented for evaluating algorithms for digital relay designs. The software includes signal processing and protection modules that are used in digital relays. The software also includes facilities for generating data for testing the performance of digital relay designs. The performance of the designs can also be studied by using data recorded at a power system bus or generated by another software. The results can be displayed in the form of graphs for visual inspection. The software is interactive and can be used with ease.< >
Specific optimization algorithms have been developed for the purpose of automated software reliability assessment tools. In this article, we propose the Monte Carlo expectation-maximization algorithm as another optimi...
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Specific optimization algorithms have been developed for the purpose of automated software reliability assessment tools. In this article, we propose the Monte Carlo expectation-maximization algorithm as another optimization algorithm, and carry out the performance comparison of the software reliability estimation algorithms through comprehensive numerical experiments.
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