The report discusses the existing problems with traditional attendance tracking methods and proposes an innovative solution to streamline the process using javaprogramming. The study also details the results and outp...
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In order to choose a set of reasonable CNC turning process parameters, a multi-step and multi process combination optimization method was proposed, in which both rough / finish machining, multiple pass in rough machin...
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A microlearning system providing up-to-date and on-time information relevant to the current work of the (bio)medical professionals was developed. The system uses a microlearning approach and adapts to the working proc...
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Dependency updates often cause compilation errors when new dependency versions introduce changes that are incompatible with existing client code. Fixing breaking dependency updates is notoriously hard, as their root c...
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This paper proposes a remote vocational education assistance system based on javalanguage and Socket language. The purpose is to improve the effect of online broadcasting, reduce the delay of sound and image transmis...
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Source code comments can improve the efficiency of software development and maintenance. However, due to the heterogeneity of natural language and program language, the quality of code comments is not so high. So, thi...
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Source code comments can improve the efficiency of software development and maintenance. However, due to the heterogeneity of natural language and program language, the quality of code comments is not so high. So, this paper proposes a novel method Code2tree, which is based on the encoder-decoder model to automatically generate java code comments. Code2tree firstly converts java source code into abstract syntax tree (AST) sequences, and then the AST sequences are encoded by GRU encoder to solve the long sequence learning dependency problem. Finally, the attention mechanism is introduced in the decoding stage, and the quality of the code comment is improved by increasing the weight of the key information. We use the open dataset java-small to train the model and verify the effectiveness of Code2tree based on common-used indicators BLEU and F1-Score.
Worked code examples are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the examp...
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This paper proposes a three-factor nonlinear regression model for java-software size estimation. java is one of the most widely used programminglanguages in the world and is actively used in the development of variou...
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This paper proposes a three-factor nonlinear regression model for java-software size estimation. java is one of the most widely used programminglanguages in the world and is actively used in the development of various software projects. The software size estimation plays a key planning role at the early stages of software project planning for the successful implementation of software development projects. The software size estimation is valuable information that is used to predict software development effort estimation using parametric models such as COCOMO, COCOMO II, COSYSMO, etc. The aim of the study is to build the three-factor nonlinear regression model for early KLOC size estimation of java-software on the basis of multivariate Johnson SB family normalizing transformations to increase the reliability and accuracy of the software size estimation at the early stage of software project planning using UML class diagram metrics. The object of the study is the process of size estimation for open-source java-software. The subject of the study is the nonlinear regression models to estimate the software size. To achieve this goal we collected software code metrics information from 571 open-source java software projects. The obtained dataset is split up in training and testing samples accordingly Cross-Validation technique. The regression model building is based on an iterative method that includes bjective multivariate normalizing transformation, multivariable outliers detection, confidence and prediction interval building. The obtained nonlinear regression model is compared to the existing models by the regression models quality criteria such as the determination coefficient, mean magnitude of relative error (MMRE) and the percentage of prediction of the relative error level 0.25 (PRED(0.25)). The comparison results confirm increasing the accuracy of the software size estimation using the obtained regression model on both training and testing samples. The prospects f
We provide support for polymorphism in static typestate analysis for object-oriented languages with upcasts and downcasts. Recent work has shown how typestate analysis can be embedded in the development of java progra...
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Concolic and many Concolic-based testing methods play an important role in saving the manual testing effort by fully automate the unit testing process. However, the performance of those methods is a big challenge when...
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