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Статті в журналах з теми "Software defect"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Kumaresh, Sakthi, and R. Baskaran. "Software Defect Prevention through Orthogonal Defect Classification (ODC)." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 3 (October 15, 2013): 2393–400. http://dx.doi.org/10.24297/ijct.v11i3.1166.

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Анотація:

â€œQuality is never an accident; it is always the result of intelligent effortâ€ [10]. In the process of making quality software product, it is necessary to have effective defect prevention process, which will minimize the risk of making defects /errors in software deliverables. An ideal approach would involve effective software development process with an integrated defect prevention process. This paper presents a Defect Prevention Model in which Defect Prevention Process(DPP) is integrated into software development life cycle to reduce the defects at early stages itself, thereby reducing the defect arrival rate as the project progresses to the subsequent stages. Orthogonal Defect Classification (ODC) scheme involving defect trigger, defect type etc. are discussed in this work to illustrate how ODC can be used in the defect prevention process. ODC can be used to measure development progress with respect to product quality and identify process problems, which will help to come out with â€œBest Practicesâ€ to be followed to eradicate the defects in the subsequent projects.

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2

Kumaresh, Sakthi, and Ramachandran Baskaran. "Mining Software Repositories for Defect Categorization." Journal of Communications Software and Systems 11, no. 1 (March 23, 2015): 31. http://dx.doi.org/10.24138/jcomss.v11i1.115.

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Анотація:

Early detection of software defects is very important to decrease the software cost and subsequently increase the software quality. Success of software industries not only depends on gaining knowledge about software defects, but largely reflects from the manner in which information about defect is collected and used. In software industries, individuals at different levels from customers to engineers apply diverse mechanisms to detect the allocation of defects to a particular class. Categorizing bugs based on their characteristics helps the Software Development team take appropriate actions to reduce similar defects that might get reported in future releases. Classification, if performed manually, will consume more time and effort. Human resource having expert testing skills & domain knowledge will be required for labeling the data. Therefore, the need of automatic classification of software defect is high.This work attempts to categorize defects by proposing an algorithm called Software Defect CLustering (SDCL). It aims at mining the existing online bug repositories like Eclipse, Bugzilla and JIRA for analyzing the defect description and its categorization. The proposed algorithm is designed by using text clustering and works with three major modules to find out the class to which the defect should be assigned. Software bug repositories hold software defect data with attributes like defect description, status, defect open and close date. Defect extraction module extracts the defect description from various bug repositories and converts it into unified format for further processing. Unnecessary and irrelevant texts are removed from defect data using data preprocessing module. Finally grouping of defect data into clusters of similar defect is done using clustering technique. The algorithm provides classification accuracy more than 80% in all of the three above mentioned repositories.

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3

Malhotra, Ruchika, and Juhi Jain. "Predicting Software Defects for Object-Oriented Software Using Search-based Techniques." International Journal of Software Engineering and Knowledge Engineering 31, no. 02 (February 2021): 193–215. http://dx.doi.org/10.1142/s0218194021500054.

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Анотація:

Development without any defect is unsubstantial. Timely detection of software defects favors the proper resource utilization saving time, effort and money. With the increasing size and complexity of software, demand for accurate and efficient prediction models is increasing. Recently, search-based techniques (SBTs) have fascinated many researchers for Software Defect Prediction (SDP). The goal of this study is to conduct an empirical evaluation to assess the applicability of SBTs for predicting software defects in object-oriented (OO) softwares. In this study, 16 SBTs are exploited to build defect prediction models for 13 OO software projects. Stable performance measures — GMean, Balance and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) are employed to probe into the predictive capability of developed models, taking into consideration the imbalanced nature of software datasets. Proper measures are taken to handle the stochastic behavior of SBTs. The significance of results is statistically validated using the Friedman test complied with Wilcoxon post hoc analysis. The results confirm that software defects can be detected in the early phases of software development with help of SBTs. This paper identifies the effective subset of SBTs that will aid software practitioners to timely detect the probable software defects, therefore, saving resources and bringing up good quality softwares. Eight SBTs — sUpervised Classification System (UCS), Bioinformatics-oriented hierarchical evolutionary learning (BIOHEL), CHC, Genetic Algorithm-based Classifier System with Adaptive Discretization Intervals (GA_ADI), Genetic Algorithm-based Classifier System with Intervalar Rule (GA_INT), Memetic Pittsburgh Learning Classifier System (MPLCS), Population-Based Incremental Learning (PBIL) and Steady-State Genetic Algorithm for Instance Selection (SGA) are found to be statistically good defect predictors.

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4

Han, Wan Jiang, He Yang Jiang, Yi Sun, and Tian Bo Lu. "Software Defect Distribution Prediction for BOSS System." Applied Mechanics and Materials 701-702 (December 2014): 67–70. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.67.

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Анотація:

Effective detection of software defects is an important activity of software development process. In this paper, we propose an approach to predict residual defects for BOSS project, which applies defect distribution model. Experiment results show that this approach can effectively improve the accuracy of defect prediction.

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5

WANG, Qing. "Software Defect Prediction." Journal of Software 19, no. 7 (October 21, 2008): 1565–80. http://dx.doi.org/10.3724/sp.j.1001.2008.01565.

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6

Zhang, Wei, Zhen Yu Ma, Wen Ge Zhang, Qing Ling Lu, and Xiao Bing Nie. "Correlation Analysis of Software Defects Density and Metrics." Applied Mechanics and Materials 713-715 (January 2015): 2225–28. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2225.

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Анотація:

It is very useful for improving software quality if we can find which software metrics are more correlative with software defects or defects density. Based on 33 actual software projects, we analyzed 44 software metrics from application level, file level, class level and function level, and do correlation analysis with the number of software defects and defect density, the results show that software metrics have little correlation with the number of software defects, but are correlative with defect density. Through correlation analysis, we selected five metrics that have larger correlation with defect density, these metrics can be used for improving software quality and predicting software defects density.

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7

CHANG, CHING-PAO. "INTEGRATING ACTION-BASED DEFECT PREDICTION TO PROVIDE RECOMMENDATIONS FOR DEFECT ACTION CORRECTION." International Journal of Software Engineering and Knowledge Engineering 23, no. 02 (March 2013): 147–72. http://dx.doi.org/10.1142/s0218194013500022.

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Анотація:

Reducing software defects is an essential activity for Software Process Improvement. The Action-Based Defect Prediction (ABDP) approach fragments the software process into actions, and builds software defect prediction models using data collected from the execution of actions and reported defects. Though the ABDP approach can be applied to predict possible defects in subsequent actions, the efficiency of corrections is dependent on the skill and knowledge of the stakeholders. To address this problem, this study proposes the Action Correction Recommendation (ACR) model to provide recommendations for action correction, using the Negative Association Rule mining technique. In addition to applying the association rule mining technique to build a High Defect Prediction Model (HDPM) to identify high defect action, the ACR builds a Low Defect Prediction Model (LDPM). For a submitted action, each HDPM rule used to predict the action as a high defect action and the LDPM rules are analyzed using negative association rule mining to spot the rule items with different characteristics in HDPM and LDPM rules. This information not only identifies the attributes required for corrections, but also provides a range (or a value) to facilitate the high defect action corrections. This study applies the ACR approach to a business software project to validate the efficiency of the proposed approach. The results show that the recommendations obtained can be applied to decrease software defect removal efforts.

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8

LIU, Hai, and Ke-gang HAO. "Defining software defect data." Journal of Computer Applications 28, no. 1 (October 14, 2008): 226–28. http://dx.doi.org/10.3724/sp.j.1087.2008.00226.

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9

Jones, C. "Software defect-removal efficiency." Computer 29, no. 4 (April 1996): 94–95. http://dx.doi.org/10.1109/2.488361.

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10

Hall, Robert J. "Editorial: software defect detection." Automated Software Engineering 17, no. 3 (May 26, 2010): 213–15. http://dx.doi.org/10.1007/s10515-010-0071-y.

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11

Falessi, Davide, Aalok Ahluwalia, and Massimiliano DI Penta. "The Impact of Dormant Defects on Defect Prediction: A Study of 19 Apache Projects." ACM Transactions on Software Engineering and Methodology 31, no. 1 (January 31, 2022): 1–26. http://dx.doi.org/10.1145/3467895.

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Анотація:

Defect prediction models can be beneficial to prioritize testing, analysis, or code review activities, and has been the subject of a substantial effort in academia, and some applications in industrial contexts. A necessary precondition when creating a defect prediction model is the availability of defect data from the history of projects. If this data is noisy, the resulting defect prediction model could result to be unreliable. One of the causes of noise for defect datasets is the presence of “dormant defects,” i.e., of defects discovered several releases after their introduction. This can cause a class to be labeled as defect-free while it is not, and is, therefore “snoring.” In this article, we investigate the impact of snoring on classifiers' accuracy and the effectiveness of a possible countermeasure, i.e., dropping too recent data from a training set. We analyze the accuracy of 15 machine learning defect prediction classifiers, on data from more than 4,000 defects and 600 releases of 19 open source projects from the Apache ecosystem. Our results show that on average across projects (i) the presence of dormant defects decreases the recall of defect prediction classifiers, and (ii) removing from the training set the classes that in the last release are labeled as not defective significantly improves the accuracy of the classifiers. In summary, this article provides insights on how to create defects datasets by mitigating the negative effect of dormant defects on defect prediction.

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12

Misirli, Ayse Tosun, Ayse Bener, and Resat Kale. "AI-Based Software Defect Predictors: Applications and Benefits in a Case Study." AI Magazine 32, no. 2 (June 5, 2011): 57. http://dx.doi.org/10.1609/aimag.v32i2.2348.

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Анотація:

Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The usage of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time and reduces the testing effort. We have built a learning-based defect prediction model for a telecommunication company in the space of one year. In this study, we have briefly explained our model, presented its pay-off and described how we have implemented the model in the company. Furthermore, we compared the performance of our model with that of another testing strategy applied in a pilot project that implemented a new process called Team Software Process (TSP). Our results show that defect predictors can predict 87 percent of code defects, decrease inspection efforts by 72 percent and hence, reduces post-release defects by 44 percent. Furthermore, they can be used as complementary tools for a new process implementation whose effects on testing activities are limited.

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13

Wang, Yan. "Efficient Prediction Method of Defect of Monitor Configuration Software." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 2 (March 20, 2019): 340–44. http://dx.doi.org/10.20965/jaciii.2019.p0340.

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Анотація:

In order to solve the problem of low efficiency in software operation, we need to research the defect prediction of monitoring configuration software. The current method has the low efficiency in the defect prediction of software. Therefore, this paper proposed the software defect prediction method based on genetic optimization support vector machines. This method carried out feature selection for the measure of complexity of software, and built software defect prediction model of genetic optimized support vector machine, and completed the research on the efficient prediction method of software defects. Experimental results show that the proposed method improves the quality of software effectively.

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14

SCHNEIDEWIND, NORMAN. "COMPLEXITY-DRIVEN RELIABILITY MODEL." International Journal of Reliability, Quality and Safety Engineering 15, no. 05 (October 2008): 479–94. http://dx.doi.org/10.1142/s0218539308003179.

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Анотація:

A model of software complexity and reliability is developed that uses an evolutionary process to transition from one software system to the next while complexity metrics are used to predict the reliability for each system. Systems are tested until the software passes defect presence criteria and is released. Testing criteria are based on defect count, defect density, and testing efficiency predictions exceeding specified thresholds. In addition, another type of testing efficiency — a directed graph representing the complexity of the software and defects embedded in the code — is used to evaluate the efficiency of defect detection in NASA satellite system software. Complexity metrics were found to be good predictors of defects and testing efficiency in this evolutionary process.

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15

Zhang, Wei, Zhen Yu Ma, Qing Ling Lu, Xiao Bing Nie, and Juan Liu. "Research on Software Defect Prediction Method Based on Machine Learning." Applied Mechanics and Materials 687-691 (November 2014): 2182–85. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.2182.

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Анотація:

This paper analyzed 44 metrics of application level, file level, class level and function level, and do correlation analysis with the number of software defects and defect density, the results show that software metrics have little correlation with the number of software defect, but are correlative with defect density. Through correlation analysis, we selected five metrics that have larger correlation with defect density. On the basis of feature selection, we predicted defect density with 16 machine learning models for 33 actual software projects. The results show that the Spearman rank correlation coefficient (SRCC) between the predicting defect density and the actual defect density based on SVR model is 0.6727, higher than other 15 machine learning models, the model that has the second absolute value of SRCC is IBk model, the SRCC only is-0.3557, the results show that the method based on SVR has the highest prediction accuracy.

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16

Liu, Can, Sumaya Sanober, Abu Sarwar Zamani, L. Rama Parvathy, Rahul Neware, and Abdul Wahab Rahmani. "Defect Prediction Technology in Software Engineering Based on Convolutional Neural Network." Security and Communication Networks 2022 (April 26, 2022): 1–8. http://dx.doi.org/10.1155/2022/5058461.

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Анотація:

Software defect prediction has become a significant study path in the field of software engineering in order to increase software reliability. Program defect predictions are being used to assist developers in identifying potential problems and optimizing testing resources to enhance program dependability. As a consequence of this strategy, the number of software defects may be predicted, and software testing resources are focused on the software modules with the most problems, allowing the defects to be addressed as soon as feasible. The author proposes a research method of defect prediction technology in software engineering based on convolutional neural network. Most of the existing defect prediction methods are based on the number of lines of code, module dependencies, stack reference depth, and other artificially extracted software features for defect prediction. Such methods do not take into account the underlying semantic features in software source code, which may lead to unsatisfactory prediction results. The author uses a convolutional neural network to mine the semantic features implicit in the source code and use it in the task of software defect prediction. Empirical studies were conducted on 5 software projects on the PROMISE dataset and using the six evaluation indicators of Recall, F1, MCC, pf, gm, and AUC to verify and analyze the experimental results showing that the AUC values of the items varied from 0.65 to 0.86. Obviously, software defect prediction experimental results obtained using convolutional neural networks are still ideal. Defect prediction model in software engineering based on convolutional neural network has high prediction accuracy.

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17

Liu, Can, Sumaya Sanober, Abu Sarwar Zamani, L. Rama Parvathy, Rahul Neware, and Abdul Wahab Rahmani. "Defect Prediction Technology in Software Engineering Based on Convolutional Neural Network." Security and Communication Networks 2022 (April 26, 2022): 1–8. http://dx.doi.org/10.1155/2022/5058461.

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Анотація:

Software defect prediction has become a significant study path in the field of software engineering in order to increase software reliability. Program defect predictions are being used to assist developers in identifying potential problems and optimizing testing resources to enhance program dependability. As a consequence of this strategy, the number of software defects may be predicted, and software testing resources are focused on the software modules with the most problems, allowing the defects to be addressed as soon as feasible. The author proposes a research method of defect prediction technology in software engineering based on convolutional neural network. Most of the existing defect prediction methods are based on the number of lines of code, module dependencies, stack reference depth, and other artificially extracted software features for defect prediction. Such methods do not take into account the underlying semantic features in software source code, which may lead to unsatisfactory prediction results. The author uses a convolutional neural network to mine the semantic features implicit in the source code and use it in the task of software defect prediction. Empirical studies were conducted on 5 software projects on the PROMISE dataset and using the six evaluation indicators of Recall, F1, MCC, pf, gm, and AUC to verify and analyze the experimental results showing that the AUC values of the items varied from 0.65 to 0.86. Obviously, software defect prediction experimental results obtained using convolutional neural networks are still ideal. Defect prediction model in software engineering based on convolutional neural network has high prediction accuracy.

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18

Prasad, V. S., and K. Sasikala. "A Study On Software Engineering Defect Prediction." Data Analytics and Artificial Intelligence 2, no. 1 (February 1, 2022): 1–6. http://dx.doi.org/10.46632/daai/2/1/1.

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Анотація:

The success of any software system entirely depends on the accuracy of the results of the system and whether it is without any flaws. Software defect prediction problems have an extremely beneficial research potential. Software defects are the major issue in any software industry. Software defects not only reduce the software quality, increase costing but it also suspends the development schedule. Software bugs lead to inaccurate and discrepant results. As an outcome of this, the software projects run late, are cancelled or become unreliable after deployment. Quality and reliability are the major challenges faced in a secure software development process. There are major software cost overruns when a software product with bugs in its various components is deployed at client s side. The software warehouse is commonly used as record keeping repository which is mostly required while adding new features or fixing bugs. Many data mining techniques and dataset repository are available to predict the software defects. Bug prediction technique is an important part in software engineering area for last one decade. Software bugs which detect at early stage are simple and inexpensive for rectifying the software. Software quality can be enhanced by using the bug prediction techniques and the software bug can be reduced if applied accurately. Dependent and independent variable are considered in Software bug prediction. To prevent defect based on software metrics software prediction model are used. Metrics based classification categorize component as defective and non-defective.

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19

CHANG, CHING-PAO, and CHIH-PING CHU. "SOFTWARE DEFECT PREDICTION USING INTERTRANSACTION ASSOCIATION RULE MINING." International Journal of Software Engineering and Knowledge Engineering 19, no. 06 (September 2009): 747–64. http://dx.doi.org/10.1142/s0218194009004428.

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Анотація:

Reducing the variance between expectation and execution of software processes is an essential activity for software development, in which the Causal Analysis is a conventional means of detecting problems in the software process. However, significant effort may be required to identify the problems of software development. Defect prevention prevents the problems from occurring, thus lowering the effort required in defect detection and correction. The prediction model is a conventional means of predicting the problems of subsequent process actions, where the prediction model can be built from the performed actions. This study proposes a novel approach that applies the Intertransaction Association Rule Mining techniques to the records of performed actions in order to discover the patterns that are likely to cause high severity defects. The discovered patterns can then be applied to predict the subsequent actions that may result in high severity defects.

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20

Huh, Sang Moo, and Woo-Je Kim. "The Derivation of Defect Priorities and Core Defects through Impact Relationship Analysis between Embedded Software Defects." Applied Sciences 10, no. 19 (October 4, 2020): 6946. http://dx.doi.org/10.3390/app10196946.

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Анотація:

As embedded software is closely related to hardware equipment, any defect in embedded software can lead to major accidents. Thus, all defects must be collected, classified, and tested based on their severity. In the pure software field, a method of deriving core defects already exists, enabling the collection and classification of all possible defects. However, in the embedded software field, studies that have collected and categorized relevant defects into an integrated perspective are scarce, and none of them have identified core defects. Therefore, the present study collected embedded software defects worldwide and identified 12 types of embedded software defect classifications through iterative consensus processes with embedded software experts. The impact relation map of the defects was drawn using the decision-making trial and evaluation laboratory (DEMATEL) method, which analyzes the influence relationship between elements. As a result of analyzing the impact relation map, the following core embedded software defects were derived: hardware interrupt, external interface, timing error, device error, and task management. All defects can be tested using this defect classification. Moreover, knowing the correct test order of all defects can eliminate critical defects and improve the reliability of embedded systems.

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21

Jindal, Rajni, Ruchika Malhotra, and Abha Jain. "Predicting Software Maintenance Effort by Mining Software Project Reports Using Inter-Version Validation." International Journal of Reliability, Quality and Safety Engineering 23, no. 06 (December 2016): 1640009. http://dx.doi.org/10.1142/s021853931640009x.

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Анотація:

Changes in the software are unavoidable due to an ever changing dynamic and active environment wherein expectations and requirements of the users tend to change rapidly. As a result, software needs to upgrade itself from its previous version to the next version in order to meet expectations of the user. The upgradation of the software is in terms of total number of Lines of Code (LOC) that might have been inserted, deleted or modified in moving from one version of software to the next. These changes are maintained in the change reports which constitute of the defect ID and defect description. Defect description describes the cause of defect which might have occurred in the previous version of the software due to which either new LOC needs to be inserted or existing LOC need to be deleted or modified. A lot of effort is required to correct the defects identified in software at the maintenance phase i.e., when software is delivered at the customers end. Thus, in this paper, we intend to predict maintenance effort by analyzing the defect reports using text mining techniques and thereafter developing the prediction models using suitable machine learning algorithms viz. Multi-Layer Perceptron (MLP), Radial-Basis Function (RBF) network and Decision Tree (DT). We have considered the changes between three successive versions of ‘MMS’ application package of Android operating system and have performed inter-version validation where the model predicted using the version ‘v’ is validated on the subsequent version i.e., ‘v+1’. The performance of the model was evaluated using Receiver Operating Characteristics (ROC) analysis. The results indicated that the model predicted on ‘MMS’ 4.0 version using MLP algorithm has shown good results when validated on ‘MMS’ 4.1 version. On the other hand, the performance of RBF and DT algorithms has been consistently average in predicting the maintenance effort.

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22

XU, Gao-Chao, Xin-Zhong LIU, Liang HU, Xiao-Dong FU, and Yu-Shuang DONG. "Software Reliability Assessment Models Incorporating Software Defect Correlation." Journal of Software 22, no. 3 (June 16, 2011): 439–50. http://dx.doi.org/10.3724/sp.j.1001.2011.03739.

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23

Khan, Muhammad Adnan, Nouh Sabri Elmitwally, Sagheer Abbas, Shabib Aftab, Munir Ahmad, Muhammad Fayaz, and Faheem Khan. "Software Defect Prediction Using Artificial Neural Networks: A Systematic Literature Review." Scientific Programming 2022 (May 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/2117339.

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Анотація:

The demand for automated online software systems is increasing day by day, which triggered the need for high-quality and maintainable softwares at lower cost. Software defect prediction is one of the crucial tasks of the quality assurance process which improves the quality at lower cost by reducing the overall testing and maintenance efforts. Early detection of defects in the software development life cycle (SDLC) leads to the early corrections and ultimately timely delivery of maintainable software, which satisfies the customer and makes him confident towards the development team. In the last decade, many machine learning-based approaches for software defect prediction have been proposed to achieve the higher accuracy. Artificial Neural Network (ANN) is considered as one of the widely used machine learning techniques, which is included in most of the proposed defect prediction frameworks and models. This research provides a critical analysis of the latest literature, published from year 2015 to 2018 on the use of Artificial Neural Networks for software defect prediction. In this study, a systematic research process is followed to extract the literature from three widely used digital libraries including IEEE, Elsevier, and Springer, and then after following a thorough process, 8 most relevant research publications are selected for critical review. This study will serve the researchers by exploring the current trends in software defect prediction with the focus on ANNs and will also provide a baseline for future innovations, comparisons, and reviews.

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24

Chen, Tse-Hsun, Weiyi Shang, Meiyappan Nagappan, Ahmed E. Hassan, and Stephen W. Thomas. "Topic-based software defect explanation." Journal of Systems and Software 129 (July 2017): 79–106. http://dx.doi.org/10.1016/j.jss.2016.05.015.

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25

Deng, Jiehan, Lu Lu, and Shaojian Qiu. "Software defect prediction via LSTM." IET Software 14, no. 4 (August 2020): 443–50. http://dx.doi.org/10.1049/iet-sen.2019.0149.

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26

Jiang, Yuan, Ming Li, and Zhi-Hua Zhou. "Software Defect Detection with Rocus." Journal of Computer Science and Technology 26, no. 2 (March 2011): 328–42. http://dx.doi.org/10.1007/s11390-011-9439-0.

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27

Qinbao Song, M. Shepperd, M. Cartwright, and C. Mair. "Software defect association mining and defect correction effort prediction." IEEE Transactions on Software Engineering 32, no. 2 (February 2006): 69–82. http://dx.doi.org/10.1109/tse.2006.1599417.

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28

Bowes, David, Tracy Hall, and Jean Petrić. "Software defect prediction: do different classifiers find the same defects?" Software Quality Journal 26, no. 2 (February 7, 2017): 525–52. http://dx.doi.org/10.1007/s11219-016-9353-3.

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29

Peng, Xuemei. "Research on Software Defect Prediction and Analysis Based on Machine Learning." Journal of Physics: Conference Series 2173, no. 1 (January 1, 2022): 012043. http://dx.doi.org/10.1088/1742-6596/2173/1/012043.

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Анотація:

Abstract The defects of machine learning prediction technology can be more comprehensive and automatic learning model to find the defects in software has become the main method of defect prediction, selection and study of algorithm is the key to improve the accuracy and efficiency of machine learning. Comparing different machine learning defect prediction methods reveals that the algorithms have different advantages in different evaluation indicators, the use of these advantages and combining the stacking ensemble learning method in machine learning is put forward different prediction algorithm of prediction results. As software metrics and again the prediction model of software defect prediction combined machine learning algorithm is based on the experiment with the model of Eclipse, the data sets show the effectiveness of the model.

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30

Wang, Xin Ping. "The Research of Software Defect Management." Applied Mechanics and Materials 378 (August 2013): 504–9. http://dx.doi.org/10.4028/www.scientific.net/amm.378.504.

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The management of defect tracking is one of important part of testing. The objective of test aims to find out the defect of software system. Therefore, the management of tracking defect, regarded as a vital part in the test, ensuring every observed defect immediately coped with. The paper discusses the objective of software defect management, the definition of defect ranking, the process of tracking defect management, and illustrates it with combining the Lotus Notes/Domino localized defect management.

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31

Bejjanki, Kiran Kumar, Jayadev Gyani, and Narsimha Gugulothu. "Class Imbalance Reduction (CIR): A Novel Approach to Software Defect Prediction in the Presence of Class Imbalance." Symmetry 12, no. 3 (March 4, 2020): 407. http://dx.doi.org/10.3390/sym12030407.

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Software defect prediction (SDP) is the technique used to predict the occurrences of defects in the early stages of software development process. Early prediction of defects will reduce the overall cost of software and also increase its reliability. Most of the defect prediction methods proposed in the literature suffer from the class imbalance problem. In this paper, a novel class imbalance reduction (CIR) algorithm is proposed to create a symmetry between the defect and non-defect records in the imbalance datasets by considering distribution properties of the datasets and is compared with SMOTE (synthetic minority oversampling technique), a built-in package of many machine learning tools that is considered a benchmark in handling class imbalance problems, and with K-Means SMOTE. We conducted the experiment on forty open source software defect datasets from PRedict or Models in Software Engineering (PROMISE) repository using eight different classifiers and evaluated with six performance measures. The results show that the proposed CIR method shows improved performance over SMOTE and K-Means SMOTE.

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32

Wang, Fang. "Software Defect Fault Intelligent Location and Identification Method Based on Data Mining." Journal of Physics: Conference Series 2146, no. 1 (January 1, 2022): 012012. http://dx.doi.org/10.1088/1742-6596/2146/1/012012.

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Abstract With the advancement of the times, computer technology is also constantly improving, and people’s requirements for software functions are also constantly improving, and as software functions become more and more complex, developers are technically limited and teamwork is not tacitly coordinated. And so on, so in the software development process, some errors and problems will inevitably lead to software defects. The purpose of this paper is to study the intelligent location and identification methods of software defects based on data mining. This article first studies the domestic and foreign software defect fault intelligent location technology, analyzes the shortcomings of traditional software defect detection and fault detection, then introduces data mining technology in detail, and finally conducts in-depth research on software defect prediction technology. Through in-depth research on several technologies, it reduces the accidents of software equipment and delays its service life. According to the experiments in this article, the software defect location proposed in this article uses two methods to compare. The first error set is used as a unit to measure the subsequent error set software error location cost. The first error set 1F contains 19 A manually injected error program, and the average positioning cost obtained is 3.75%.

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33

Hewett, Rattikorn. "Mining software defect data to support software testing management." Applied Intelligence 34, no. 2 (September 19, 2009): 245–57. http://dx.doi.org/10.1007/s10489-009-0193-8.

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34

Armah, Gabriel Kofi, Guanchun Luo, Ke Qin, and Angolo Shem Mbandu. "Applying Variant Variable Regularized Logistic Regression for Modeling Software Defect Predictor." Lecture Notes on Software Engineering 4, no. 2 (May 2016): 107–15. http://dx.doi.org/10.7763/lnse.2016.v4.234.

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35

Mabayoje, Modinat Abolore, Abdullateef Olwagbemiga Balogun, Hajarah Afor Jibril, Jelili Olaniyi Atoyebi, Hammed Adeleye Mojeed, and Victor Elijah Adeyemo. "Parameter tuning in KNN for software defect prediction: an empirical analysis." Jurnal Teknologi dan Sistem Komputer 7, no. 4 (August 10, 2019): 121–26. http://dx.doi.org/10.14710/jtsiskom.7.4.2019.121-126.

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Software Defect Prediction (SDP) provides insights that can help software teams to allocate their limited resources in developing software systems. It predicts likely defective modules and helps avoid pitfalls that are associated with such modules. However, these insights may be inaccurate and unreliable if parameters of SDP models are not taken into consideration. In this study, the effect of parameter tuning on the k nearest neighbor (k-NN) in SDP was investigated. More specifically, the impact of varying and selecting optimal k value, the influence of distance weighting and the impact of distance functions on k-NN. An experiment was designed to investigate this problem in SDP over 6 software defect datasets. The experimental results revealed that k value should be greater than 1 (default) as the average RMSE values of k-NN when k>1(0.2727) is less than when k=1(default) (0.3296). In addition, the predictive performance of k-NN with distance weighing improved by 8.82% and 1.7% based on AUC and accuracy respectively. In terms of the distance function, kNN models based on Dilca distance function performed better than the Euclidean distance function (default distance function). Hence, we conclude that parameter tuning has a positive effect on the predictive performance of k-NN in SDP.

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36

Memon, Mashooque Ahmed, Mujeeb-ur-Rehman Maree Baloch, Muniba Memon, and Syed Hyder Abbas Musavi. "A Regression Analysis Based Model for Defect Learning and Prediction in Software Development." July 2021 40, no. 3 (July 1, 2021): 617–29. http://dx.doi.org/10.22581/muet1982.2103.15.

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The development of software undergoes multiple regression phases to deliver quality software. Therefore, to minimize the development effort, time and cost it is very important to understand the probable defects associated with the designed modules. It is possible that occurrence of a range of defects may impact the designed modules which need to be predicted in advance to have a close inter-association with the depended modules. Most of the existing defect prediction classifier mechanisms are derived from the past project data learning, but it is not sufficient for new project defect predicting as the new design may have a different kind of parameters and constraints. This paper recommends Regression Analysis (RA) based defect learning and prediction Defect Prediction (RA-DP) mechanism to support the defective or non-defective prediction for quality software development. The RA-DP approach provides two methods to perform this prediction analysis. It initially presents an association learning through RA to construct the regression rules from the learned knowledge required for the defect prediction. The constructed regression rules are used for defect prediction and analysis. To measure the performance of the RA-DP a regression experimental evaluation is performed over the defect-prone PROMISE dataset from NASA project. The outcome of the results is analyzed through measuring the prediction Accuracy, Sensitivity and Specificity to demonstrate the improvisation and effectiveness of the proposal in comparison to a few existing classifiers.

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37

Yao, Tianwen, Ben Zhang, Jun Peng, Zhiqiang Han, Zhaobing Yang, Zhi Zhang, and Bo Zhang. "Defect Prediction Technology of Aerospace Software Based on Deep Neural Network and Process Measurement." Mathematical Problems in Engineering 2022 (February 1, 2022): 1–8. http://dx.doi.org/10.1155/2022/1276830.

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In order to ensure high reliability, the efficiency of traditional aerospace software testing is often low. With the rapid development of machine learning, its powerful data feature extraction ability has great potential in improving the efficiency of aerospace software testing. Therefore, this paper proposed a software defect prediction method based on deep neural network and process measurement. Based on the NASA data set and combined with the software process data, the software defect measurement set is constructed. 35 measurement elements are used as the original input, and multiple single-layer automatic coding networks are superimposed to form the deep neural network model of software defect. The model is finally trained by the layer-by-layer greedy training method to realize software defect prediction. Experimental verification shows that the prediction method has a good prediction effect on aerospace software defects, and the accuracy rate reached 90%, which can greatly improve the efficiency and effect of aerospace software testing.

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38

Manivasagam, G., and R. Gunasundari. "An optimized feature selection using fuzzy mutual information based ant colony optimization for software defect prediction." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 456. http://dx.doi.org/10.14419/ijet.v7i1.1.9954.

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In recent years, there is a significant notification focused towards the prediction of software defect in the field of software engineering. The prediction of software defects assist in reducing the cost of testing effort, improving the process of software testing and to concentrate only on the fault-prone software modules. Recently, software defect prediction is an important research topic in the software engineering field. One of the important factors which effect the software defect detection is the presence of noisy features in the dataset. The objective of this proposed work is to contribute an optimization technique for the selection of potential features to improve the prediction capability of software defects more accurately. The Fuzzy Mutual Information Ant Colony Optimization is used for searching the optimal feature set with the ability of Meta heuristic search. This proposed feature selection efficiency is evaluated using the datasets from NASA metric data repository. Simulation results have indicated that the proposed method makes an impressive enhancement in the prediction of routine for three different classifiers used in this work.

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39

Vashisht, Rohit, and Syed Afzal Murtaza Rizvi. "Estimation of Target Defect Prediction Coverage in Heterogeneous Cross Software Projects." International Journal of Information System Modeling and Design 12, no. 1 (January 2021): 73–93. http://dx.doi.org/10.4018/ijismd.2021010104.

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Heterogeneous cross-project defect prediction (HCPDP) is an evolving area under quality assurance domain which aims to predict defects in a target project that has restricted historical defect data as well as completely non-uniform software metrics from other projects using a model built on another source project. The article discusses a particular source project group's problem of defect prediction coverage (DPC) and also proposes a novel two phase model for addressing this issue in HCPDP. The study has evaluated DPC on 13 benchmarked datasets in three open source software projects. One hundred percent of DPC is achieved with higher defect prediction accuracy for two project group pairs. The issue of partial DPC is found in third prediction pairs and a new strategy is proposed in the research study to overcome this issue. Furthermore, this paper compares HCPDP modeling with reference to with-in project defect prediction (WPDP), both empirically and theoretically, and it is found that the performance of WPDP is highly comparable to HCPDP and gradient boosting method performs best among all three classifiers.

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40

LUO, Guangchun, Ying MA, and Ke QIN. "Active Learning for Software Defect Prediction." IEICE Transactions on Information and Systems E95.D, no. 6 (2012): 1680–83. http://dx.doi.org/10.1587/transinf.e95.d.1680.

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41

Noor, Rida, and Muhammad Fahad Khan. "Defect Management in Agile Software Development." International Journal of Modern Education and Computer Science 6, no. 3 (March 8, 2014): 55–60. http://dx.doi.org/10.5815/ijmecs.2014.03.07.

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42

Gordieiev, O. O., and K. P. Leontiev. "Life Cycle Model of Software Defect." Mathematical and computer modelling. Series: Technical sciences, no. 21 (November 2, 2020): 51–60. http://dx.doi.org/10.32626/2308-5916.2020-21.51-60.

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43

Arora, Ishani, Vivek Tetarwal, and Anju Saha. "Open Issues in Software Defect Prediction." Procedia Computer Science 46 (2015): 906–12. http://dx.doi.org/10.1016/j.procs.2015.02.161.

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44

Felix, Ebubeogu Amarachukwu, and Sai Peck Lee. "Integrated Approach to Software Defect Prediction." IEEE Access 5 (2017): 21524–47. http://dx.doi.org/10.1109/access.2017.2759180.

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45

Qiao, Lei, Xuesong Li, Qasim Umer, and Ping Guo. "Deep learning based software defect prediction." Neurocomputing 385 (April 2020): 100–110. http://dx.doi.org/10.1016/j.neucom.2019.11.067.

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46

Huang, Bing, Zongquan Ma, and Jinghui Li. "Overcoming obstacles to software defect prevention." International Journal of Industrial and Systems Engineering 24, no. 4 (2016): 529. http://dx.doi.org/10.1504/ijise.2016.080290.

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47

Huang, Bing, Zongquan Ma, and Jinghui Li. "Overcoming obstacles to software defect prevention." International Journal of Industrial and Systems Engineering 24, no. 4 (2016): 529. http://dx.doi.org/10.1504/ijise.2016.10000334.

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48

McConnell, S. "Gauging software readiness with defect tracking." IEEE Software 14, no. 3 (1997): 136, 135. http://dx.doi.org/10.1109/52.589257.

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49

Esteves, Geanderson, Eduardo Figueiredo, Adriano Veloso, Markos Viggiato, and Nivio Ziviani. "Understanding machine learning software defect predictions." Automated Software Engineering 27, no. 3-4 (October 12, 2020): 369–92. http://dx.doi.org/10.1007/s10515-020-00277-4.

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50

Hewett, Rattikorn, and Phongphun Kijsanayothin. "On modeling software defect repair time." Empirical Software Engineering 14, no. 2 (May 6, 2008): 165–86. http://dx.doi.org/10.1007/s10664-008-9064-x.

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