Software Defect Prediction Method Based on Stable Learning

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.

Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes

A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion,while simultaneously receiving stimuli from the environment and controlling some part of a human brain or body.Incoming visual information can be processed by the brain in millisecond intervals.The retina computes visual scenes and sends its output to the cortex in the form of neuronal spikes for further computation.Thus,the neuronal signal of interest for a retinal neuroprosthesis is the neuronal spike.Closed-loop computation in a neuroprosthesis includes two stages:encoding a stimulus as a neuronal signal,and decoding it back into a stimulus.In this paper,we review some of the recent progress that has been achieved in visual computation models that use spikes to analyze natural scenes that include static images and dynamic videos.We hypothesize that in order to obtain a better understanding of the computational principles in the retina,a hypercircuit view of the retina is necessary,in which the different functional network motifs that have been revealed in the cortex neuronal network are taken into consideration when interacting with the retina.The different building blocks of the retina,which include a diversity of cell types and synaptic connections-both chemical synapses and electrical synapses(gap junctions)-make the retina an ideal neuronal network for adapting the computational techniques that have been developed in artificial intelligence to model the encoding and decoding of visual scenes.An overall systems approach to visual computation with neuronal spikes is necessary in order to advance the next generation of retinal neuroprosthesis as an artificial visual system.

A GAN-EfficientNet-Based Traceability Method for Malicious Code Variant Families

Due to the diversity and unpredictability of changes in malicious code,studying the traceability of variant families remains challenging.In this paper,we propose a GAN-EfficientNetV2-based method for tracing families of malicious code variants.This method leverages the similarity in layouts and textures between images of malicious code variants from the same source and their original family of malicious code images.The method includes a lightweight classifier and a simulator.The classifier utilizes the enhanced EfficientNetV2 to categorize malicious code images and can be easily deployed on mobile,embedded,and other devices.The simulator utilizes an enhanced generative adversarial network to simulate different variants of malicious code and generates datasets to validate the model’s performance.This process helps identify model vulnerabilities and security risks,facilitating model enhancement and development.The classifier achieves 98.61%and 97.59%accuracy on the MMCC dataset and Malevis dataset,respectively.The simulator’s generated image of malicious code variants has an FID value of 155.44 and an IS value of 1.72±0.42.The classifier’s accuracy for tracing the family of malicious code variants is as high as 90.29%,surpassing that of mainstream neural network models.This meets the current demand for high generalization and anti-obfuscation abilities in malicious code classification models due to the rapid evolution of malicious code.

基于Visual Studio Code的实验教学模式研究——以“微机原理”实验教学为例

为探索出适用于一流本科课程教学的实践方式,文章针对"微机原理"实验课程,提出基于Visual Studio Code的实验教学模式。新模式弥补了传统实验教学的不足,能有效增强学生编写代码的兴趣,满足个性化需求,提高编程效率,提升教学质量。文章从实验项目安排、编程软件安装和实验操作等方面多角度介绍新模式对实验教学的支撑作用,为软件编程方面的实验教学工作提供新思路,进一步推进先进信息技术与实验教学的深度融合。

基于Visual Studio Code的C语言程序设计实践教学探索

针对当前主流C语言实践环境对实践教学支持不够的问题,提出基于Visual Studio Code的实践教学观点,阐述其从兴趣引导、编程乐趣和编程效率提升等方面如何有效地支撑C语言实践教学,说明新环境有更好的教学效果。

基于Visual C++的同步盘应用

随着计算机软件工程技术的不断的飞速发展,网络进入了每一个人的生活中,各种网络应用软件的不断地融入到人们的日常生活中,人们无论工作与生活,都离不开互联网。在大潮流中,同步盘的应用变得越来越重要。文章主要是通过介绍同步盘应用的开发方法及用Visual C++实现的具体步骤。

微信“睡眠助手”小程序的设计与实现

设计一款基于微信小程序的“睡眠助手”,主要功能模块有个人中心、浏览睡眠常识、设置睡眠闹钟、播放睡眠音乐、睡眠录音与播放及睡眠分析表等。采用微信开发者工具实现系统前端页面,运用Visual StudioCode搭建后台,利用DBeaver数据库管理工具创建MySQL数据库并存放数据。用户可以在小程序中浏览信息,利用各个功能对自身的睡眠质量有所了解并加以改善。该小程序具有操作简单、界面清晰、管理方便、功能完备等优势,具有很高的应用和推广价值。