Software Defect(软件缺陷)研究综述
Software Defect 软件缺陷 - Timely detection of software defects favors the proper resource utilization saving time, effort and money. [1] Thus, when applying these appaoraches into radar software testing and validation, the precision rate and recall rate of defect classification are poor and have effect on the reuse effectiveness of software defects. [2] The rational management of software defects and possible failures is a fundamental requirement for a mature software industry. [3] This study provides a new in-depth examination of the impact of class size on the relationship between OO metrics and software defects or defect-proneness. [4] We envision our access control framework to complement existing operating system access control frameworks, to significantly reduce the dimension of data required for machine learning, and to build extra resilience into the operating systems against damages caused by either malware or software defects. [5] The use of traditional machine learning techniques can automate the prediction of software defects. [6] For the task of reducing the risk of software defects, we show initial results from a successful case study that offers more actionable software analytics. [7] This struggle translates into software defects and lower developer productivity. [8] Software Quality Assurance (SQA) planning aims to define proactive plans, such as defining maximum file size, to prevent the occurrence of software defects in future releases. [9] Thus, when applying these appaoraches into radar software testing and validation, the precision rate and recall rate of defect classification are poor and have effect on the reuse effectiveness of software defects. [10] In the paper, static analysis software is taken as the research object, the errors or failures caused by the potential defects of the software modules are analyzed, and a software analysis method based on big data tendency prediction is proposed to use the software defects of the stacked noise reduction sparse analyzer to predict. [11] Then, actively analyzes the weaknesses, defects or vulnerabilities in the software to detect the design vulnerabilities and software defects in the intelligent electric meter, gives qualitative or quantitative test and verification results for its fault tolerance and ease of recovery, and establishes a complete software reliability and safety testing system, and develops a prototype system, which has good guiding significance and implementation value for the software reliability and safety testing of intelligent electric meters. [12] Early detection of software defects becomes a must, various methods being researched. [13] The error in the software system can affect the efficiency and reliability of the software therefore, early prediction of software defects can save the companies from a bigger loss. [14] In the process of using computer application software, the system may fail because of software defects. [15]及时检测软件缺陷有利于正确利用资源，从而节省时间、精力和金钱。 [1] 因此，将这些方法应用于雷达软件测试和验证时，缺陷分类的准确率和召回率较差，影响了软件缺陷的重用效果。 [2] 对软件缺陷和可能出现的故障进行合理的管理是成熟软件产业的基本要求。 [3] 本研究对班级规模对 OO 指标与软件缺陷或缺陷倾向之间关系的影响进行了新的深入研究。 [4] 我们设想我们的访问控制框架是对现有操作系统访问控制框架的补充，以显着减少机器学习所需的数据维度，并为操作系统构建额外的弹性，以抵御恶意软件或软件缺陷造成的损害。 [5] 使用传统的机器学习技术可以自动预测软件缺陷。 [6] 对于降低软件缺陷风险的任务，我们展示了一个成功案例研究的初步结果，该案例研究提供了更多可操作的软件分析。 [7] 这种斗争转化为软件缺陷和降低开发人员的生产力。 [8] 软件质量保证 (SQA) 计划旨在定义主动计划，例如定义最大文件大小，以防止在未来版本中出现软件缺陷。 [9] 因此，将这些方法应用于雷达软件测试和验证时，缺陷分类的准确率和召回率较差，影响了软件缺陷的重用效果。 [10] 文中以静态分析软件为研究对象，分析软件模块潜在缺陷导致的错误或故障，提出一种基于大数据趋势预测的软件分析方法，利用软件模块的软件缺陷。堆叠降噪稀疏分析器进行预测。 [11] 然后，主动分析软件中的弱点、缺陷或漏洞，检测智能电表的设计漏洞和软件缺陷，对其容错性和易恢复性给出定性或定量的测试验证结果，建立完整的软件可靠性和安全测试系统，开发了原型系统，对智能电表的软件可靠性和安全测试具有很好的指导意义和实施价值。 [12] 早期检测软件缺陷成为必须，正在研究各种方法。 [13] 软件系统中的错误会影响软件的效率和可靠性，因此，早期预测软件缺陷可以为企业避免更大的损失。 [14] 在使用计算机应用软件的过程中，系统可能会因为软件缺陷而出现故障。 [15]
software development life 软件开发生活
Software defect prediction (SDP) is a convenient way to identify defects in the early phases of the software development life cycle. [1] Software defect prediction (SDP) in the initial period of the software development life cycle (SDLC) remains a critical and important assignment. [2] Identifying software defects during early stages of Software Development life cycle reduces the project effort and cost. [3] Software defect prediction is important to identify defects in the early phases of software development life cycle. [4] Predicting software defects in the early stages of the software development life cycle, such as the design and requirement analysis phase, provides significant economic advantages for software companies. [5]软件缺陷预测 (SDP) 是一种在软件开发生命周期的早期阶段识别缺陷的便捷方法。 [1] 软件开发生命周期（SDLC）初始阶段的软件缺陷预测（SDP）仍然是一项至关重要的任务。 [2] nan [3] nan [4] nan [5]
class imbalance problem 阶级不平衡问题
Context: Generally, there are more non-defective instances than defective instances in the datasets used for software defect prediction (SDP), which is referred to as the class imbalance problem. [1] In this proposed system, we are applying a Machine learning approach for Prediction of Software Defects to overcome from class imbalance problem. [2] (Recommended by Professor Hyunsook Do) The fourth paper, by Zeinab Eivazpour and Mohammad Reza Keyvanpour, concerns the cost issue when handling the class imbalance problem over the training dataset in software defect prediction. [3]背景：通常，用于软件缺陷预测（SDP）的数据集中无缺陷实例多于缺陷实例，这被称为类不平衡问题。 [1] 在这个提议的系统中，我们正在应用一种机器学习方法来预测软件缺陷，以克服类不平衡问题。 [2] nan [3]
object oriented software
This paper aims at providing further evidence on the usefulness of centrality measures as indicators of software defect proneness by: (1) investigating the relationships between object-oriented metrics and centrality measures, and (2) exploring how they can be combined to improve the prediction of fault-prone classes in object-oriented software. [1] In terms of the security problem of power information system, this paper analysed the importance of the software defect prediction method in object-oriented software development, and proposed a software prediction model based on particle swarm optimized Support Vector Machine (SVM) corresponding to the features of object-oriented software. [2]Predicting Software Defect 预测软件缺陷
To optimize the process of software testing and to improve software quality and reliability, many attempts have been made to develop more effective methods for predicting software defects. [1] Lines of code (LOC) have been among the most popular measures for predicting software defects, but so far, their ultimate use has not been adequately understood. [2] A lot of research has been progressed in predicting software defects using machine learning approaches. [3] Predicting software defects in the early stages of the software development life cycle, such as the design and requirement analysis phase, provides significant economic advantages for software companies. [4]为了优化软件测试过程并提高软件质量和可靠性，人们进行了许多尝试来开发更有效的软件缺陷预测方法。 [1] 代码行 (LOC) 一直是预测软件缺陷的最流行措施之一，但到目前为止，它们的最终用途尚未得到充分了解。 [2] nan [3] nan [4]
Time Software Defect
Just-in-time software defect prediction (JIT-SDP) aims to conduct software defect prediction (SDP) on commit-level code changes to achieve effective SQA resource allocation. [1] Just-in-Time Software Defect Prediction (JIT-SDP) is an SDP approach that makes defect predictions at the software change level. [2] To address the above problem, in this paper, we adopt the effort-aware just-in-time software defect prediction (JIT-SDP), which is a typical hand-crafted-feature-based task, as an example, to exploit new possible solutions. [3]即时软件缺陷预测 (JIT-SDP) 旨在对提交级别的代码更改进行软件缺陷预测 (SDP)，以实现有效的 SQA 资源分配。 [1] nan [2] nan [3]
software defect prediction 软件缺陷预测
The prediction of software artifacts on defect‐prone (DP) or non‐defect‐prone (NDP) classes during the testing phase helps minimize software business costs, which is a classification task in software defect prediction (SDP) field. [1] Many supervised machine learning algorithms have been used over the past few decades for software defect prediction. [2] Software defect prediction heavily relies on the metrics collected from software projects. [3] Software defect prediction (SDP) is a convenient way to identify defects in the early phases of the software development life cycle. [4] Context: Generally, there are more non-defective instances than defective instances in the datasets used for software defect prediction (SDP), which is referred to as the class imbalance problem. [5] Software defect prediction is a crucial software project management activity to enhance the software quality. [6] Software defect prediction has been a concurrent topic in software quality-based research. [7] With the growing number of software applications being developed for every small challenge, the importance of devising efficient software defect prediction models is imperative. [8] Software defect prediction has an important role to play in improving the quality of programming and helps to reduce the time and cost of programming testing. [9] In this paper, the characteristics of software defect prediction are analyzed from the perspective of machine learning. [10] Deep learning-based software defect prediction has been popular these days. [11] In result, researchers are unable to learn much from systems or they are unable to find or determine prediction or take decision for respective applications like fraud detection, rare diseases identification/ prediction, approval of credit card, software defect prediction, etc. [12] Software defect prediction (SDP) is a very important way for analyzing software quality and reducing development costs. [13] Imbalanced data and feature redundancies are common problems in many fields, especially in software defect prediction, data mining, machine learning, and industrial big data application. [14] Traditional research methods in software defect prediction use part of the data in the same project to train the defect prediction model and predict the defect label of the remaining part of the data. [15] Software defect prediction models are classifiers that are constructed from historical software data. [16] Just-in-time software defect prediction (JIT-SDP) aims to conduct software defect prediction (SDP) on commit-level code changes to achieve effective SQA resource allocation. [17] Software defect prediction has been widely used in software system development, among which the method based on machine learning has proved to be more effective. [18] Context: Software engineering researchers have undertaken many experiments investigating the potential of software defect prediction algorithms. [19] To ensure software quality, software defect prediction plays a prominent role for the software developers and practitioners. [20] A novel approach for software defect prediction of unlabeled datasets is proposed using modified objective cluster analysis (OCA). [21] Identifying and reporting the defect probe areas is the main job of software defect prediction techniques. [22] (Recommended by Professor Hyunsook Do) The fourth paper, by Zeinab Eivazpour and Mohammad Reza Keyvanpour, concerns the cost issue when handling the class imbalance problem over the training dataset in software defect prediction. [23] The paper is devoted to the study of the software defect prediction process using deep learning algorithms. [24] Aiming at the problem of grammar and semantic information understanding of the network structure of software system, this paper proposes a method of software defect prediction, which is based on Transformer model, which is completely dependent on self-attention mechanism, it can embed key information in the code semantics of the end-to-end learning software modules. [25] Software defect prediction is an effective approach to save testing resources and improve software quality, which is widely studied in the field of software engineering. [26] These results present a cogent case for the use of oversampling prior to applying deep learning on software defect prediction datasets. [27] This paper mainly analyzes the characteristics of software defect prediction from the perspective of machine learning, and proposes a semi-supervised software defect prediction method based on sampling and integration for the problem of class imbalance in software defect data and the incomplete classification of data sets. [28] Software defect prediction aims at helping developers allocate existing resources by predicting defect-prone modules prior to the testing phase. [29] Software defect prediction recommends the most defect-prone software modules for optimization of the test resource allocation. [30] Keywords—Software quality metrics, Software defect prediction, Software fault prediction, Machine learning. [31] The purpose of software defect prediction is to improve the quality of a software project by building a predictive model to decide whether a software module is or is not fault prone. [32] To ensure software reliability, software defect prediction (SDP) techniques are employed to help developers effectively allocate the testing resources. [33] Software defect prediction (SDP) is an important means to assist developers in discovering and repairing potential defects that may endanger software security in advance and improving software security and reliability. [34] Software defect prediction (SDP) in the initial period of the software development life cycle (SDLC) remains a critical and important assignment. [35] Software defect prediction, aimed at assisting software practitioners in allocating test resources more efficiently, predicts the potential defective modules in software products. [36] The training data commonly used in software defect prediction (SDP) usually contains some instances that have similar values on features but are in different classes, which significantly degrades the performance of prediction models trained using these instances. [37] Software defect prediction is an integral part of the software development process. [38] Researchers have a keen interest in producing machine learning models for effective and accurate software defect prediction in the early stages of software development. [39] Software Defect Prediction is a significant angle to guarantee programming quality. [40] Recent advances in the domain of software defect prediction (SDP) include the integration of multiple classification techniques to create an ensemble or hybrid approach. [41] Software defect prediction has an important role to play in improving the quality of programming and helps to reduce the time and cost of programming testing. [42] This study proposes novel rank aggregation-based multi-filter feature selection (FS) methods to address high dimensionality and filter rank selection problem in software defect prediction (SDP). [43] Software defect prediction (SDP) can help developers reasonably allocate limited resources for locating bugs and prioritizing their testing efforts. [44] Deep belief networks cannot eliminate the noise and missing value, which affect the accuracy of the software defect prediction (SDP) model. [45] com : The approaches associated with software defect prediction are used to reduce the time and cost of discovering software defects in source code and to improve the software quality in the organizations. [46] Feature selection is known to be an applicable solution to address the problem of high dimensionality in software defect prediction (SDP). [47] The importance of software defect prediction has risen significantly over the past decade and is an inseparable part of software quality. [48] Software defect prediction (SDP) can be used to produce reliable, high-quality software. [49] Software defect prediction is one of the hot research topics in the software engineering application. [50]在测试阶段对易缺陷（DP）或非易缺陷（NDP）类的软件工件进行预测有助于最大限度地降低软件业务成本，这是软件缺陷预测（SDP）领域的分类任务。 [1] 在过去的几十年中，许多监督机器学习算法已被用于软件缺陷预测。 [2] 软件缺陷预测很大程度上依赖于从软件项目中收集的指标。 [3] 软件缺陷预测 (SDP) 是一种在软件开发生命周期的早期阶段识别缺陷的便捷方法。 [4] 背景：通常，用于软件缺陷预测（SDP）的数据集中无缺陷实例多于缺陷实例，这被称为类不平衡问题。 [5] 软件缺陷预测是提高软件质量的一项重要的软件项目管理活动。 [6] 软件缺陷预测一直是基于软件质量的研究中的一个共同课题。 [7] nan [8] 软件缺陷预测在提高编程质量方面发挥着重要作用，有助于减少编程测试的时间和成本。 [9] 本文从机器学习的角度分析了软件缺陷预测的特点。 [10] 如今，基于深度学习的软件缺陷预测已经很流行。 [11] 结果，研究人员无法从系统中学到很多东西，或者他们无法找到或确定预测或针对欺诈检测、罕见疾病识别/预测、信用卡批准、软件缺陷预测等相应应用做出决策。 [12] 软件缺陷预测（SDP）是分析软件质量和降低开发成本的一种非常重要的方法。 [13] 数据不平衡和特征冗余是许多领域的普遍问题，尤其是在软件缺陷预测、数据挖掘、机器学习和工业大数据应用等领域。 [14] 软件缺陷预测中的传统研究方法利用同一项目中的部分数据来训练缺陷预测模型并预测剩余部分数据的缺陷标签。 [15] 软件缺陷预测模型是根据历史软件数据构建的分类器。 [16] 即时软件缺陷预测 (JIT-SDP) 旨在对提交级别的代码更改进行软件缺陷预测 (SDP)，以实现有效的 SQA 资源分配。 [17] 软件缺陷预测在软件系统开发中得到了广泛的应用，其中基于机器学习的方法被证明更为有效。 [18] 背景：软件工程研究人员进行了许多实验来研究软件缺陷预测算法的潜力。 [19] 为保证软件质量，软件缺陷预测对于软件开发者和从业者来说起着突出的作用。 [20] 提出了一种使用改进的目标聚类分析 (OCA) 对未标记数据集进行软件缺陷预测的新方法。 [21] 识别和报告缺陷探测区域是软件缺陷预测技术的主要工作。 [22] nan [23] 本文致力于研究使用深度学习算法的软件缺陷预测过程。 [24] 针对软件系统网络结构的语法和语义信息理解问题，提出一种基于Transformer模型的软件缺陷预测方法，该方法完全依赖于self-attention机制，可以嵌入关键信息在端到端学习软件模块的代码语义中。 [25] 软件缺陷预测是节省测试资源、提高软件质量的有效途径，在软件工程领域得到广泛研究。 [26] 这些结果为在对软件缺陷预测数据集应用深度学习之前使用过采样提供了一个令人信服的案例。 [27] 本文主要从机器学习的角度分析软件缺陷预测的特点，针对软件缺陷数据类别不平衡和数据集分类不完整的问题，提出一种基于采样和集成的半监督软件缺陷预测方法。 [28] 软件缺陷预测旨在通过在测试阶段之前预测容易出现缺陷的模块来帮助开发人员分配现有资源。 [29] 软件缺陷预测推荐最容易出现缺陷的软件模块，以优化测试资源分配。 [30] 关键词——软件质量指标、软件缺陷预测、软件故障预测、机器学习。 [31] 软件缺陷预测的目的是通过建立预测模型来确定软件模块是否容易出错，从而提高软件项目的质量。 [32] 为了保证软件的可靠性，软件缺陷预测（SDP）技术被用来帮助开发人员有效地分配测试资源。 [33] 软件缺陷预测（SDP）是帮助开发人员提前发现和修复可能危及软件安全的潜在缺陷，提高软件安全可靠性的重要手段。 [34] 软件开发生命周期（SDLC）初始阶段的软件缺陷预测（SDP）仍然是一项至关重要的任务。 [35] 软件缺陷预测，旨在帮助软件从业者更有效地分配测试资源，预测软件产品中潜在的缺陷模块。 [36] nan [37] nan [38] nan [39] nan [40] nan [41] nan [42] nan [43] nan [44] nan [45] nan [46] nan [47] nan [48] nan [49] nan [50]
software defect datum
Due to drift in software defect data, prediction model performances may degrade over time. [1] Software fault prediction is one of such tasks that predicts the fault proneness of the developed modules by applying machine learning techniques on software defect data. [2] The imbalanced nature of class in software defect data, which including intra-class imbalance and inter-classes imbalance, increases the difficulty of learning an effective defect prediction model. [3] However, the high dimensionality and class distribution imbalance of software defect data seriously affect software defect prediction performance. [4]由于软件缺陷数据的漂移，预测模型的性能可能会随着时间的推移而下降。 [1] nan [2] nan [3] nan [4]
software defect detection
However, Cost-Sensitive Forest is relatively new to the literature and originally invented to solve imbalance problems in software defect detection. [1] The accuracy comparison, the performance of the different metrics can broadly help in software defect detection mechanism. [2]然而，成本敏感森林在文献中相对较新，最初是为了解决软件缺陷检测中的不平衡问题而发明的。 [1] nan [2]
software defect localization
At present, most software defect localization methods focus on single defect localization, but few on multi-defect localization. [1] Software defect localization is the key to reduce and remove software defects. [2]目前，大多数软件缺陷定位方法都侧重于单缺陷定位，而很少关注多缺陷定位。 [1] nan [2]
software defect severity 软件缺陷严重性
These computed vectors are used as an input of the software defect severity level prediction models and ensemble techniques like Bagging, Random Forest classifier, Extra Trees classifier, AdaBoost and Gradient Boosting have been used to train these models. [1] Software defect severity level helps to indicate the impact of bugs on the execution of the software and how rapidly these bugs need to be addressed by the team. [2]这些计算的向量被用作软件缺陷严重性级别预测模型的输入，并且已经使用了 Bagging、随机森林分类器、额外树分类器、AdaBoost 和梯度提升等集成技术来训练这些模型。 [1] 软件缺陷严重性级别有助于指示错误对软件执行的影响以及团队需要多快解决这些错误。 [2]