Search Results (8)

With the growing reliance on software, the frequency of software bugs has increased significantly. To address these issues, users or developers typically submit bug reports, which developers analyze and resolve. However, many submitted bug reports are duplicates of previously reported issues, creating inefficiencies in the bug resolution process. To enhance developer productivity, an automatic method for detecting duplicate bug reports is essential. In this study, we present a novel approach for identifying duplicate and nonduplicate bug reports using feature learning through correlation analysis. Our method utilizes bug report features, including product and component information, extracted from bug repositories. The process begins with preprocessing the bug reports to ensure data quality. Next, a feature selection algorithm identifies relevant features, which are then used to train a machine learning model based on bidirectional encoder representations from transformers (BERT). The proposed model’s effectiveness was evaluated across multiple datasets: Apache, JDT, Platform, KDE, Core, Firefox, and Thunderbird. Our results show detection accuracies of 91.41%, 88.66%, 86.08%, 92.94%, 90.68%, 88.25%, and 91.62%, respectively. These outcomes represent a significant improvement of 32% to 41% compared to baseline models, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), convolutional LSTMs (CNN-LSTMs), Naive Bayes classifiers, and random forest classifiers. Our findings show that the proposed model is highly effective for duplicate bug report prediction and offers substantial advancements over existing methods. This approach has the potential to streamline bug management processes and improve overall software development efficiency. Full article

Many insects attack date palm trees but date palm trees in the Sultanate are particularly under threat due to the spread of pests and the Dubas bug (Db). Date palm productivity in Oman has been reduced by 28% due to Db infestation. The manual field detection of these pests requires huge efforts and costs, making field surveys time consuming and difficult. In this context, remote sensing integrated with deep learning techniques can help in the early detection of Db infestation. A total of 240 date palms with corrected geospatial locations and coordinates and their health status were systematically recorded throughout the 66-square-kilometer study area. We used advanced remote sensing tools and deep learning techniques to detect individual palm trees and their health levels in terms of Db infestation. Very-high-resolution (50 cm) satellite images rendered in visible and NIR bands were used as datasets to delineate and identify individual tree positions and determine their health condition. Our proposed method resulted in an overall accuracy of 87% for the detection of date palm trees and 85% for the detection of health levels of the plants. The overall detection accuracy of high and low infestation levels was observed with high precision at 95% and 93%, respectively. Hence, we can conclude with confidence that our technique performed well by accurately detecting individual date palm trees and determining their level of Db infestation. The approach used in this study can also provide farmers with useful knowledge regarding the Db risk and damage control for better management of Db. Moreover, the model used in this study may also lay the foundations for other models to detect infested plants and trees other than date palms. Full article

One of the most prominent issues the ever-growing open-source software community faces is the abundance of buggy code. Well-established version control systems and repository hosting services such as GitHub and Maven provide a checks-and-balances structure to minimize the amount of buggy code introduced. Although these platforms are effective in mitigating the problem, it still remains. To further the efforts toward a more effective and quicker response to bugs, we must understand the factors that affect the time it takes to fix one. We apply a custom traversal algorithm to commits made for open source repositories to determine when “simple stupid bugs” were first introduced to projects and explore the factors that drive the time it takes to fix them. Using the commit history from the main development branch, we are able to identify the commit that first introduced 13 different types of simple stupid bugs in 617 of the top Java projects on GitHub. Leveraging a statistical survival model and other non-parametric statistical tests, we found that there were two main categories of categorical variables that affect a bug’s life; Time Factors and Author Factors. We find that bugs are fixed quicker if they are introduced and resolved by the same developer. Further, we discuss how the day of the week and time of day a buggy code was written and fixed affects its resolution time. These findings will provide vital insight to help the open-source community mitigate the abundance of code and can be used in future research to aid in bug-finding programs. Full article

This paper presents a real-time global path planning method for mobile robots using harmonic functions, such as the Poisson equation, based on the Proper Generalized Decomposition (PGD) of these functions. The main property of the proposed technique is that the computational cost is negligible in real-time, even if the robot is disturbed or the goal is changed. The main idea of the method is the off-line generation, for a given environment, of the whole set of paths from any start and goal configurations of a mobile robot, namely the computational vademecum, derived from a harmonic potential field in order to use it on-line for decision-making purposes. Up until now, the resolution of the Laplace or Poisson equations has been based on traditional numerical techniques unfeasible for real-time calculation. This drawback has prevented the extensive use of harmonic functions in autonomous navigation, despite their powerful properties. The numerical technique that reverses this situation is the Proper Generalized Decomposition. To demonstrate and validate the properties of the PGD-vademecum in a potential-guided path planning framework, both real and simulated implementations have been developed. Simulated scenarios, such as an L-Shaped corridor and a benchmark bug trap, are used, and a real navigation of a LEGO^®MINDSTORMS robot running in static environments with variable start and goal configurations is shown. This device has been selected due to its computational and memory-restricted capabilities, and it is a good example of how its properties could help the development of social robots. Full article

River valleys of the Central European Lowlands are the zones of the highest dynamics of morphogenic processes. In the case of areas affected by glacial processes, despite their lowland nature, often they also have a complex geological structure. Sub-alluvial bedrock, composed of erosion-resistant deposits, commonly forms morphological protrusions within them. Their presence significantly affects both the course of flood flows and the valley floor relief. Effective forecasting of fluvial processes in such valley reaches requires conducting research within the entire geomorphologically active zone, both in the channel and the floodplain. The effectiveness of such research should be enhanced by simultaneous use of several different remote sensing methods, including short-range remote sensing. The verification of this hypothesis was the aim of the presented works. Such methods were used in the study of morphodynamics of a Bug valley reach. This area is characterized by a complex geological structure. High-resolution multispectral satellite images (VHRs) and a digital elevation model (DEM) based on aerial laser scanning (ALS) were used to examine the terrain relief. The morphology of the river channel itself was determined based on a series of bathymetric measurements made by a research team. Due to induced climate change and increasing maximum flow values, it can be assumed that the effect of a geological structure in the Central European Lowlands will play an increasing role. The threat and losses associated with floods will also increase. Rational flood prevention requires improvement of remote sensing research methods in lowland river valleys, especially those with complex geological structures. The valley reach presented in this article is an example of such a landform. Full article

The Dubas bug Ommatissus lybicus is a serious pest of date palms. The infestation level of the Dubas bug varies from location to location, as well as from one season to the next. Climate factors are considered to be the main drivers for fluctuations in infestation levels. Few studies have examined the effects of solar radiation on O. lybicus infestation. This study was undertaken to examine the effect of solar radiation on O. lybicus infestation levels in Oman. Infestation data were collected during the spring infestation seasons of 2009 and 2016 from 49 and 69 locations, respectively, from seven governorates of North Oman. The monthly clear-sky potential solar radiation was calculated from a digital elevation model (DEM) with 20-m resolution in the ArcGIS environment, and the average daily solar radiation was calculated for each month. Ordinary least square regression (OLS) and geographic weight regression (GWR) models were run to find the relationship between infestation levels and solar radiation. The infestation level ranged from 0.02 insect/leaflet to 32.98 insects/leaflet, with an average of 7.50 insects/leaflet in 2009 and 0.17 insect/leaflet to 17.52 insects/leaflet, with an average of 4.38 insects/leaflet in 2016. The highest solar radiation was recorded in June, with an average of 27.7 MJ/m²/day, and the minimum was in December, with an average of 14.1 MJ/m²/day. The higher infestation rate showed a weak correlation with solar radiation. Full article

Date palm trees, Phoenix dactylifera, are the primary crop in Oman. Most date palm cultivation is under the traditional agricultural system. The plants are usually under dense planting, which makes them prone to pest infestation. The main pest attacking date palm crops in Oman is the Dubas bug Ommatissus lybicus. This study integrated modern technology, remote sensing and geographic information systems to determine the number of date palm trees in traditional agriculture locations to find the relationship between date palm tree density and O. lybicus infestation. A local maxima method for tree identification was used to determine the number of date palm trees from high spatial resolution satellite imagery captured by WorldView-3 satellite. Window scale sizes of 3, 5 and 7 m were tested and the results showed that the best window size for date palm trees number detection was 7 m, with an overall estimation accuracy 88.2%. Global regression ordinary least square (OLS) and local geographic weighted regression (GWR) were used to test the relationship between infestation intensity and tree density. The GWR model showed a good positive significant relationship between infestation and tree density in the spring season with R² = 0.59 and medium positive significant relationship in the autumn season with R² = 0.30. In contrast, the OLS model results showed a weak positive significant relationship in the spring season with R² = 0.02, p < 0.05 and insignificant relationship in the autumn season with R² = 0.01, p > 0.05. The results indicated that there was a geographic effect on the infestation of O. lybicus, which had a greater impact on infestation severity, and that the impact of tree density was higher in the spring season than in autumn season. Full article

With the increasing number of bug reports coming into the open bug repository, it is impossible to triage bug reports manually by software managers. This paper proposes a novel approach called En-LDA (Entropy optimized Latent Dirichlet Allocation (LDA)) for automatic bug report assignment. Specifically, we propose entropy to optimize the number of topics of the LDA model and further use the entropy optimized LDA to capture the expertise and interest of developers in bug resolution. A developer’s interest in a topic is modeled by the number of the developer’s comments on bug reports of the topic divided by the number of all the developer’s comments. A developer’s expertise in a topic is modeled by the number of the developer’s comments on bug reports of the topic divided by the number of all developers’ comments on the topic. Given a new bug report, En-LDA recommends a ranked list of developers who are potentially adequate to resolve the new bug. Experiments on Eclipse JDT and Mozilla Firefox projects show that En-LDA can achieve high recall up to 84% and 58%, and precision up to 28% and 41%, respectively, which indicates promising aspects of the proposed approach. Full article