Journal articles: 'Defective software'

1

Lloyd, I. "Liability for defective software." Reliability Engineering & System Safety 32, no. 1-2 (January 1991): 193–207. http://dx.doi.org/10.1016/0951-8320(91)90054-b.

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Guzdial, Mark, and Bertrand Meyer. "Understanding CS1 students; defective software." Communications of the ACM 55, no. 1 (January 2012): 14–15. http://dx.doi.org/10.1145/2063176.2063180.

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Hedley, Steve. "Defective Software in the Court of Appeal." Cambridge Law Journal 56, no. 1 (March 1997): 21–25. http://dx.doi.org/10.1017/s000819730001761x.

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Parry, Rex. "Who bears the risk of defective software?" Computer Audit Update 1996, no. 12 (December 1996): 27–29. http://dx.doi.org/10.1016/s0960-2593(97)80803-8.

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Rakitin, S. R. "Coping with Defective Software in Medical Devices." Computer 39, no. 4 (April 2006): 40–45. http://dx.doi.org/10.1109/mc.2006.123.

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Wan, Hongyan, Guoqing Wu, Mali Yu, and Mengting Yuan. "Software Defect Prediction Based on Cost-Sensitive Dictionary Learning." International Journal of Software Engineering and Knowledge Engineering 29, no. 09 (September 2019): 1219–43. http://dx.doi.org/10.1142/s0218194019500384.

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Software defect prediction technology has been widely used in improving the quality of software system. Most real software defect datasets tend to have fewer defective modules than defective-free modules. Highly class-imbalanced data typically make accurate predictions difficult. The imbalanced nature of software defect datasets makes the prediction model classifying a defective module as a defective-free one easily. As there exists the similarity during the different software modules, one module can be represented by the sparse representation coefficients over the pre-defined dictionary which consists of historical software defect datasets. In this study, we make use of dictionary learning method to predict software defect. We optimize the classifier parameters and the dictionary atoms iteratively, to ensure that the extracted features (sparse representation) are optimal for the trained classifier. We prove the optimal condition of the elastic net which is used to solve the sparse coding coefficients and the regularity of the elastic net solution. Due to the reason that the misclassification of defective modules generally incurs much higher cost risk than the misclassification of defective-free ones, we take the different misclassification costs into account, increasing the punishment on misclassification defective modules in the procedure of dictionary learning, making the classification inclining to classify a module as a defective one. Thus, we propose a cost-sensitive software defect prediction method using dictionary learning (CSDL). Experimental results on the 10 class-imbalance datasets of NASA show that our method is more effective than several typical state-of-the-art defect prediction methods.

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Tomar, Divya, and Sonali Agarwal. "Prediction of Defective Software Modules Using Class Imbalance Learning." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/7658207.

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Software defect predictors are useful to maintain the high quality of software products effectively. The early prediction of defective software modules can help the software developers to allocate the available resources to deliver high quality software products. The objective of software defect prediction system is to find as many defective software modules as possible without affecting the overall performance. The learning process of a software defect predictor is difficult due to the imbalanced distribution of software modules between defective and nondefective classes. Misclassification cost of defective software modules generally incurs much higher cost than the misclassification of nondefective one. Therefore, on considering the misclassification cost issue, we have developed a software defect prediction system using Weighted Least Squares Twin Support Vector Machine (WLSTSVM). This system assigns higher misclassification cost to the data samples of defective classes and lower cost to the data samples of nondefective classes. The experiments on eight software defect prediction datasets have proved the validity of the proposed defect prediction system. The significance of the results has been tested via statistical analysis performed by using nonparametric Wilcoxon signed rank test.

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Huhryanskaya, E., and M. Surikova. "Mathematical software for automation systems of defective whips bucking." Актуальные направления научных исследований XXI века: теория и практика 3, no. 2 (May 3, 2015): 364–68. http://dx.doi.org/10.12737/11115.

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Mehrez, Ahmed. "Reassessing Software Quality Performance." International Journal of Knowledge Management 10, no. 1 (January 2014): 58–77. http://dx.doi.org/10.4018/ijkm.2014010104.

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Software quality has always been described as a poorly developed construct. Several reports and much evidence show clear problems related to software quality. This research empirically tests if ineffective implementation of knowledge management activities would be a reason behind possible existence of defective quality performance in the software industry. The main finding shows that knowledge management would directly affect quality performance in the Egyptian software industry. Statistical correlation is significant between the two constructs; knowledge management and quality performance.

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Mishra, Bharavi, and K. K. Shukla. "Mining Attributes Patterns of Defective Modules for Object Oriented Software." International Journal of Computer Applications 54, no. 11 (September 25, 2012): 14–18. http://dx.doi.org/10.5120/8610-2462.

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Riquelme, J. C., R. Ruiz, D. Rodriguez, and J. S. Aguilar-Ruiz. "Finding Defective Software Modules by Means of Data Mining Techniques." IEEE Latin America Transactions 7, no. 3 (July 2009): 377–82. http://dx.doi.org/10.1109/tla.2009.5336637.

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Alsukhni, Emad, Ahmad A. Saifan, and Hanadi Alawneh. "A New Data Mining-Based Framework to Test Case Prioritization Using Software Defect Prediction." International Journal of Open Source Software and Processes 8, no. 1 (January 2017): 21–41. http://dx.doi.org/10.4018/ijossp.2017010102.

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Test cases do not have the same importance when used to detect faults in software; therefore, it is more efficient to test the system with the test cases that have the ability to detect the faults. This research proposes a new framework that combines data mining techniques to prioritize the test cases. It enhances fault prediction and detection using two different techniques: 1) the data mining regression classifier that depends on software metrics to predict defective modules, and 2) the k-means clustering technique that is used to select and prioritize test cases to identify the fault early. Our approach of test case prioritization yields good results in comparison with other studies. The authors used the Average Percentage of Faults Detection (APFD) metric to evaluate the proposed framework, which results in 19.9% for all system modules and 25.7% for defective ones. Our results give us an indication that it is effective to start the testing process with the most defective modules instead of testing all modules arbitrary arbitrarily.

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Cauvery, G., and D. DhinaSuresh. "Software Defect Prediction Using Machine Learning Techniques." Data Analytics and Artificial Intelligence 3, no. 2 (January 1, 2023): 30–33. http://dx.doi.org/10.46632/daai/3/2/7.

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Software defect prediction provides development groups with observable outcomes while contributing to industrial results and development faults predicting defective code areas can help developers identify bugs and organize their test activities. The percentage of classification providing the proper prediction is essential for early identification. Moreover, software- defected data sets are supported and at least partially recognized due to their enormous dimension.

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Dong, Quande, and Xu Xu. "Control Policy for Unreliable Production System Producing Defective Items." Journal of Software Engineering 8, no. 4 (September 15, 2014): 375–86. http://dx.doi.org/10.3923/jse.2014.375.386.

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Babishov, Elnur Megraliyevich, Gennady Vladimirovich Pakhomov, Vladimir Alekseevich Shulgin, Evgeny Yur’evich Buslov, and Dmitry Anatol’evich Minakov. "Hardware-Software Complex for Laser Scanning in Color Sorting Machines." Modern Applied Science 8, no. 5 (September 26, 2014): 262. http://dx.doi.org/10.5539/mas.v8n5p262.

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The paper concerns the problem of grain mixture analysis based on processing images synthesized during line-by-line scanning of each object in color sorting systems. The paper presents a hardware-software complex for sorting objects in the real time. The hardware part of the complex consists of two blocks: light source and device for reception and processing of images. The light source is a laser, passed through optical fiber and linearly expanded across the entire width of the photoseparator’s chute. Linear laser scan produces significant intensity of illumination. It is sufficient for working on the transmission of radiation through objects. The software part of the complex also consists of two blocks: the thresholding algorithm and an automated program for finding the optimal parameters of sorting on the basis of that algorithm. The algorithm calculates the number of connected defective pixels with arbitrary shape. Automated program works on the basis of pre-formed images of the objects of two classes: good and defective. As a result, the program displays in tabular form the most optimal sorting parameters. The program shows the dependence of the loss of good product from the missouts of the defective objects. The customer gets a clear choice of the most suitable sorting results. This complex was tested for sorting of unshelled rice seeds via transmission. It is shown that the complex allows to effectively detect hidden seed defects: red pigmentation, immaturity, fungal diseases, and others.

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Pujianto, Utomo, and . "Random Forest and Novel Under-Sampling Strategy for Data Imbalance in Software Defect Prediction." International Journal of Engineering & Technology 7, no. 4.15 (October 7, 2018): 39. http://dx.doi.org/10.14419/ijet.v7i4.15.21368.

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Data imbalance is one among characteristics of software quality data sets that can have a negative effect on the performance of software defect prediction models. This study proposed an alternative to random under-sampling strategy by using only a subset of non-defective data which have been calculated as having biggest distance value to the centroid of defective data. Combined with random forest classification, the proposed method outperformed both the random under-sampling and non-sampling method on the basis of accuracy, AUC, f-measure, and true positive rate performance measures.

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Hammad, Mustafa. "Classifying defective software projects based on machine learning and complexity metrics." International Journal of Computing Science and Mathematics 13, no. 4 (2021): 401. http://dx.doi.org/10.1504/ijcsm.2021.10040983.

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Hammad, Mustafa. "Classifying defective software projects based on machine learning and complexity metrics." International Journal of Computing Science and Mathematics 13, no. 4 (2021): 401. http://dx.doi.org/10.1504/ijcsm.2021.117600.

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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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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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Ishiguro, Mizuki, Rui Fukui, Shin’ichi Warisawa, Naoyasu Narita, and Hironobu Miyoshi. "Laser Cutting Defect Recognition Using Conversion of Processing Light Information into Spectrogram Images – Spectroscopic Measurements in Multiple Work Surface Conditions and Extraction of Spectral Data Features Based on Processing Principle –." International Journal of Automation Technology 15, no. 5 (September 5, 2021): 728–39. http://dx.doi.org/10.20965/ijat.2021.p0728.

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At urban production sites, laser cutting is an essential technology for high-speed flexible sheet-metal processing. This study aims to detect defective cuts by sensing laser-cutting-induced light emission and elucidate meaningful features for processing-based detection. The proposed method comprises three steps. In the first step, the sensors installed in the laser head acquire the spectra of light generated during processing, and data analysis software converts the spectral data into spectrograms and stacked-graph images. In the second step, image processing software extracts the edges of both images and emphasizes the periodic features in normal laser cutting. In the final step, a one-class support vector machine recognizes defective cuts from the extracted features. Verification tests using multiple normal and abnormal cut data confirmed that the proposed method accurately detected defective cuts.

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Krammer, Josef G., Ernst G. Bernard, Matthias Sauer, and Josef A. Nossek. "Sorting on defective VLSI-arrays." Integration 12, no. 1 (November 1991): 33–48. http://dx.doi.org/10.1016/0167-9260(91)90041-i.

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Gautam, Prerna, Sumit Maheshwari, Ahmad Hasan, Amrina Kausar, and Chandra K. Jaggi. "Optimal inventory strategies for an imperfect production system with advertisement and price reliant demand under rework option for defectives." RAIRO - Operations Research 56, no. 1 (January 2022): 183–97. http://dx.doi.org/10.1051/ro/2021188.

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Every industrial sector emits carbon emissions during production, which is always a major concern for environmental practitioners. To resolve this, innovative and smart ways are required that are not only cost-effective but also environmentally savvy. Moreover, the production processes are also prone to various imperfections, and thus the production of defectives is quite pragmatic. The handling of such defective products varies from industry to industry and type of products. The present study constructs an inventory model to handle the defective products via a proficient rework strategy that makes the product fit to be sold at the primary price. Here, the demand for the product is assumed to be dependent on the selling price and advertisements. The model also considers the energy usage during the production and the cost of carbon emissions while optimizing the production batch size and selling price. Optimality is proved graphically by using the software Mathematica 11.3.0. A comprehensive sensitivity analysis for all the parameters is performed to impart managerial insights and robustness to the model.

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Vandehei, Bailey, Daniel Alencar Da Costa, and Davide Falessi. "Leveraging the Defects Life Cycle to Label Affected Versions and Defective Classes." ACM Transactions on Software Engineering and Methodology 30, no. 2 (March 2021): 1–35. http://dx.doi.org/10.1145/3433928.

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Two recent studies explicitly recommend labeling defective classes in releases using the affected versions (AV) available in issue trackers (e.g., Jira). This practice is coined as the realistic approach . However, no study has investigated whether it is feasible to rely on AVs. For example, how available and consistent is the AV information on existing issue trackers? Additionally, no study has attempted to retrieve AVs when they are unavailable. The aim of our study is threefold: (1) to measure the proportion of defects for which the realistic method is usable, (2) to propose a method for retrieving the AVs of a defect, thus making the realistic approach usable when AVs are unavailable, (3) to compare the accuracy of the proposed method versus three SZZ implementations. The assumption of our proposed method is that defects have a stable life cycle in terms of the proportion of the number of versions affected by the defects before discovering and fixing these defects. Results related to 212 open-source projects from the Apache ecosystem, featuring a total of about 125,000 defects, reveal that the realistic method cannot be used in the majority (51%) of defects. Therefore, it is important to develop automated methods to retrieve AVs. Results related to 76 open-source projects from the Apache ecosystem, featuring a total of about 6,250,000 classes, affected by 60,000 defects, and spread over 4,000 versions and 760,000 commits, reveal that the proportion of the number of versions between defect discovery and fix is pretty stable (standard deviation <2)—across the defects of the same project. Moreover, the proposed method resulted significantly more accurate than all three SZZ implementations in (i) retrieving AVs, (ii) labeling classes as defective, and (iii) in developing defects repositories to perform feature selection. Thus, when the realistic method is unusable, the proposed method is a valid automated alternative to SZZ for retrieving the origin of a defect. Finally, given the low accuracy of SZZ, researchers should consider re-executing the studies that have used SZZ as an oracle and, in general, should prefer selecting projects with a high proportion of available and consistent AVs.

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Nguyen, Quoc Toan. "Defective sewing stitch semantic segmentation using DeeplabV3+ and EfficientNet." Inteligencia Artificial 25, no. 70 (November 24, 2022): 64–76. http://dx.doi.org/10.4114/intartif.vol25iss70pp64-76.

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Defective stitch inspection is an essential part of garment manufacturing quality assurance. Traditional mechanical defect detection systems are effective, but they are usually customized with handcrafted features that must be operated by a human. Deep learning approaches have recently demonstrated exceptional performance in a wide range of computer vision applications. The requirement for precise detail evaluation, combined with the small size of the patterns, undoubtedly increases the difficulty of identification. Therefore, image segmentation (semantic segmentation) was employed for this task. It is identified as a vital research topic in the field of computer vision, being indispensable in a wide range of real-world applications. Semantic segmentation is a method of labeling each pixel in an image. This is in direct contrast to classification, which assigns a single label to the entire image. And multiple objects of the same class are defined as a single entity. DeepLabV3+ architecture, with encoder-decoder architecture, is the proposed technique. EfficientNet models (B0-B2) were applied as encoders for experimental processes. The encoder is utilized to encode feature maps from the input image. The encoder's significant information is used by the decoder for upsampling and reconstruction of output. Finally, the best model is DeeplabV3+ with EfficientNetB1 which can classify segmented defective sewing stitches with superior performance (MeanIoU: 94.14%).

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Salt, J. D. "The seven habits of highly defective simulation projects." Journal of Simulation 2, no. 3 (November 2008): 155–61. http://dx.doi.org/10.1057/jos.2008.7.

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Shaikh, Samir, and Sham Kulkarni. "A theoretical model for predicting the vibration response of outer race defective ball bearing." International Journal of Engineering & Technology 7, no. 2 (March 13, 2018): 289. http://dx.doi.org/10.14419/ijet.v7i2.8953.

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The theoretical model with 2 degree-of-freedom system is developed for predicting the vibration response and analyze frequency properties in an extended type defective ball bearing. In the mathematical formulation, the contact between the races and rolling element considered as non-linear springs. The contact forces produced during the collaboration of rolling elements are obtained by utilizing Hertzian contact deformation hypothesis. The second order nonlinear differential equation of motion is solved using a state space variable method with the help of MATLAB software and the vibration acceleration response of the defective ball bearing presented in the frequency spectrum. The effects of variation in speed and size of the defect on characteristic frequency of extended fault on the outer raceway of the ball bearing have been investigated. The theoretical results of the healthy (non defective) and defective bearing are compared with each other.

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Huang, Tao, and Chih-Chiang Fang. "Optimization of Software Test Scheduling under Development of Modular Software Systems." Symmetry 15, no. 1 (January 9, 2023): 195. http://dx.doi.org/10.3390/sym15010195.

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Software testing and debugging is a crucial part of the software development process since defective software not only incurs customer dissatisfaction but also might incur legal issues. However, the managers of a software development company cannot arbitrarily prolong their software debugging period due to their software testing budget and opportunity in the market. Accordingly, in order to propose an advantageous testing project, the managers should be aware of the influence of the testing project on cost, quality, and time to make the best decision. In this study, a new software reliability growth model (SRGM) with consideration of the testing staff’s learning effect is proposed to achieve better prediction. The methods of estimating the model’s parameters and the symmetric confidence intervals are also proposed in the study. Moreover, in the past, most of the SRGMs focused on a single software system. However, in practice, some software systems were developed using modular-based system engineering approaches. Therefore, traditional software testing work can be changed to multiple modular testing work in this scenario. Therefore, the manager can use this to dispatch multiple staff groups to perform the individual testing work simultaneously. The study proposes two mathematical programming models to handle the scheduling of modular testing work. Additionally, the design of a computerized decision support system is also proposed in the study for the application in practice.

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Prasad, V. S., and K. Sasikala. "Software Defect Prediction and Software Quality Assessment Using Dlr-Lvq and Fuzzy Rules." Electrical and Automation Engineering 1, no. 1 (April 1, 2022): 21–27. http://dx.doi.org/10.46632/eae/1/1/4.

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Recently, Software development has been considerably grown. Fault in the software causes fault and interrupts the output. Characteristics like these make it much challenging to avert software flaws. Spontaneously forecasting the amount of flaws within the software modules is essential and also can assist developers to proficiently allot restricted resources. Recently, numerous Software Defect Prediction (SDP) techniques are developed. But, the accuracy and time consuming challenges still remain to be solved. Also, a few top-notch techniques don't properly classify the software whereas it is a needed metric to ensure quality standards. This work proffers a novel Decaying Learning Rate – Learning vector Quantization (DLR-LVQ) classifier to forecast the software defect. The proposed methods consist of the following steps: redundant data removal, feature extraction (FE), feature oversampling, data normalization, defect prediction (DP), and quality prediction. The proposed DLR-LVQ’s attained outcome is assessed with the existent methodologies. The outcomes exhibit that the methodology proposed attains efficient classification outcomes are examined. Keywords: Software Defect Prediction (SDP), Non defective software quality prediction, BM-SMOTE, Decaying Learning Rate, Learning Vector Quantization, Fuzzy rules, HDFS and Map Reduce.

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Chen, Liqiong, Shilong Song, and Can Wang. "A Novel Effort Measure Method for Effort-Aware Just-in-Time Software Defect Prediction." International Journal of Software Engineering and Knowledge Engineering 31, no. 08 (August 2021): 1145–69. http://dx.doi.org/10.1142/s0218194021500364.

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Just-in-time software defect prediction (JIT-SDP) is a fine-grained software defect prediction technology, which aims to identify the defective code changes in software systems. Effort-aware software defect prediction is a software defect prediction technology that takes into consideration the cost of code inspection, which can find more defective code changes in limited test resources. The traditional effort-aware defect prediction model mainly measures the effort based on the number of lines of code (LOC) and rarely considers additional factors. This paper proposes a novel effort measure method called Multi-Metric Joint Calculation (MMJC). When measuring the effort, MMJC takes into account not only LOC, but also the distribution of modified code across different files (Entropy), the number of developers that changed the files (NDEV) and the developer experience (EXP). In the simulation experiment, MMJC is combined with Linear Regression, Decision Tree, Random Forest, LightGBM, Support Vector Machine and Neural Network, respectively, to build the software defect prediction model. Several comparative experiments are conducted between the models based on MMJC and baseline models. The results show that indicators ACC and [Formula: see text] of the models based on MMJC are improved by 35.3% and 15.9% on average in the three verification scenarios, respectively, compared with the baseline models.

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Saifan, Ahmad A., and Zainab Lataifeh. "Privacy preserving defect prediction using generalization and entropy-based data reduction." Intelligent Data Analysis 25, no. 6 (October 29, 2021): 1369–405. http://dx.doi.org/10.3233/ida-205504.

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The software engineering community produces data that can be analyzed to enhance the quality of future software products, and data regarding software defects can be used by data scientists to create defect predictors. However, sharing such data raises privacy concerns, since sensitive software features are usually considered as business assets that should be protected in accordance with the law. Early research efforts on protecting the privacy of software data found that applying conventional data anonymization to mask sensitive attributes of software features degrades the quality of the shared data. In addition, data produced by such approaches is not immune to attacks such as inference and background knowledge attacks. This research proposes a new approach to share protected release of software defects data that can still be used in data science algorithms. We created a generalization (clustering)-based approach to anonymize sensitive software attributes. Tomek link and AllNN data reduction approaches were used to discard noisy records that may affect the usefulness of the shared data. The proposed approach considers diversity of sensitive attributes as an important factor to avoid inference and background knowledge attacks on the anonymized data, therefore data discarded is removed from both defective and non-defective records. We conducted experiments conducted on several benchmark software defect datasets, using both data quality and privacy measures to evaluate the proposed approach. Our findings showed that the proposed approach outperforms existing well-known techniques using accuracy and privacy measures.

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Akimova, Elena N., Alexander Yu Bersenev, Artem A. Deikov, Konstantin S. Kobylkin, Anton V. Konygin, Ilya P. Mezentsev, and Vladimir E. Misilov. "A Survey on Software Defect Prediction Using Deep Learning." Mathematics 9, no. 11 (May 24, 2021): 1180. http://dx.doi.org/10.3390/math9111180.

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Defect prediction is one of the key challenges in software development and programming language research for improving software quality and reliability. The problem in this area is to properly identify the defective source code with high accuracy. Developing a fault prediction model is a challenging problem, and many approaches have been proposed throughout history. The recent breakthrough in machine learning technologies, especially the development of deep learning techniques, has led to many problems being solved by these methods. Our survey focuses on the deep learning techniques for defect prediction. We analyse the recent works on the topic, study the methods for automatic learning of the semantic and structural features from the code, discuss the open problems and present the recent trends in the field.

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DeHon, A., and H. Naeimi. "Seven Strategies for Tolerating Highly Defective Fabrication." IEEE Design and Test of Computers 22, no. 4 (April 2005): 306–15. http://dx.doi.org/10.1109/mdt.2005.94.

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S. Mohan Reddy, S., M. Vinod Kumar, B. Sanjay, and K. Aruna Kumari. "Comparative Analysis of Edge Feeding and coaxial Feeding Technique with Fixed Frequency." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 848. http://dx.doi.org/10.14419/ijet.v7i2.7.11080.

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The performance of rectangular microstrip patch antenna with the edge feeding and coaxial feeding techniques with DGS (Defective ground structure) and DSS (Defective substrate structure) are analyzed. It was observed that the return loss for Edge feeding is -28.39dB, for DSS it is - 26.44 and return loss for coaxial with DGS is -27.50dB and for DSS is -28.52. VSWR is approximately equal to 1 for all designs and also it was observed that the Gain is enhancement in Edge feeding and Bandwidth is improvement with coaxial feeding. The antenna designing and simulation is done by using the ANSOFT HFSS Software.

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Bashir, Kamal, Tianrui Li, and Mahama Yahaya. "A Novel Feature Selection Method Based on Maximum Likelihood Logistic Regression for Imbalanced Learning in Software Defect Prediction." International Arab Journal of Information Technology 17, no. 5 (September 1, 2020): 721–30. http://dx.doi.org/10.34028/iajit/17/5/5.

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The most frequently used machine learning feature ranking approaches failed to present optimal feature subset for accurate prediction of defective software modules in out-of-sample data. Machine learning Feature Selection (FS) algorithms such as Chi-Square (CS), Information Gain (IG), Gain Ratio (GR), RelieF (RF) and Symmetric Uncertainty (SU) perform relatively poor at prediction, even after balancing class distribution in the training data. In this study, we propose a novel FS method based on the Maximum Likelihood Logistic Regression (MLLR). We apply this method on six software defect datasets in their sampled and unsampled forms to select useful features for classification in the context of Software Defect Prediction (SDP). The Support Vector Machine (SVM) and Random Forest (RaF) classifiers are applied on the FS subsets that are based on sampled and unsampled datasets. The performance of the models captured using Area Ander Receiver Operating Characteristics Curve (AUC) metrics are compared for all FS methods considered. The Analysis Of Variance (ANOVA) F-test results validate the superiority of the proposed method over all the FS techniques, both in sampled and unsampled data. The results confirm that the MLLR can be useful in selecting optimal feature subset for more accurate prediction of defective modules in software development process

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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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Voroshilov, Aleksandr, and Polina Buyvol. "Application of intelligent analysis to identify defective vehicle components." MATEC Web of Conferences 341 (2021): 00027. http://dx.doi.org/10.1051/matecconf/202134100027.

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The article shows the possibility of using intelligent analysis in a vehicle service when assessing the vehicle reliability. It was hypothesized that the use of association rules in diagnostics can increase the speed of repairs and the quality of customer service, allowing to identify the nodes that are highly likely to be faulty at the same time. For this, a knowledge base was built from the patterns obtained by applying association rules to the vehicle failure statistics. An application was implemented, which, on its basis, issues recommendations to the repair worker to check certain nodes based on the already identified defective nodes entered into the program. The proposed technique, together with the developed software tool, will optimize the diagnostic processes.

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Hardoni, Andre, Dian Palupi Rini, and Sukemi Sukemi. "Integrasi SMOTE pada Naive Bayes dan Logistic Regression Berbasis Particle Swarm Optimization untuk Prediksi Cacat Perangkat Lunak." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 1 (January 22, 2021): 233. http://dx.doi.org/10.30865/mib.v5i1.2616.

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Software defects are one of the main contributors to information technology waste and lead to rework, thus consuming a lot of time and money. Software defect prediction has the objective of defect prevention by classifying certain modules as defective or not defective. Many researchers have conducted research in the field of software defect prediction using NASA MDP public datasets, but these datasets still have shortcomings such as class imbalance and noise attribute. The class imbalance problem can be overcome by utilizing SMOTE (Synthetic Minority Over-sampling Technique) and the noise attribute problem can be solved by selecting features using Particle Swarm Optimization (PSO), So in this research, the integration between SMOTE and PSO is applied to the classification technique machine learning naïve Bayes and logistic regression. From the results of experiments that have been carried out on 8 NASA MDP datasets by dividing the dataset into training and testing data, it is found that the SMOTE + PSO integration in each classification technique can improve classification performance with the highest AUC (Area Under Curve) value on average 0,89 on logistic regression and 0,86 in naïve Bayes in the training and at the same time better than without combining the two.

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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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Li, Tze Fen, and Shui-Ching Chang. "Classification on defective items using unidentified samples." Pattern Recognition 38, no. 1 (January 2005): 51–58. http://dx.doi.org/10.1016/j.patcog.2004.05.008.

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Oßner, Christopher, Erik Buchmann, and Klemens Böhm. "Identifying defective nodes in wireless sensor networks." Distributed and Parallel Databases 34, no. 4 (January 18, 2016): 591–610. http://dx.doi.org/10.1007/s10619-015-7189-7.

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Palit, Ajoy K., Kishore K. Duganapalli, and Walter Anheier. "Crosstalk fault modeling in defective pair of interconnects." Integration 41, no. 1 (January 2008): 27–37. http://dx.doi.org/10.1016/j.vlsi.2007.04.005.

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Zhu, Sheng, Fan Jun Meng, and De Ma Ba. "The Remanufacturing System Based on Robot MAG Surfacing." Key Engineering Materials 373-374 (March 2008): 400–403. http://dx.doi.org/10.4028/www.scientific.net/kem.373-374.400.

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A remanufacturing system based on robot MAG surfacing has been developed recently. In this paper, the work principle, functions and composition of this system are introduced. A worn metal part to be remanufactured should be preprocessed firstly, and the defective model of the part gained by reversing engineering technology is compared with normal model of the metal part, then the prototyping path layout is carried out combined with MAG welding process, finally the remanufacturing prototyping is implemented. The remanufacturing system is composed of robotic system (as executing machine), 3D laser scanner (as reversing scanning device), digital pulse MAG welding power source (as prototyping equipment), desk computer (as central control unit) and software modules that support various functions. The functions of the remanufacturing system comprise calibration of system, part reversing measurement, data processing, defective model reconstruction, welding remanufacturing prototyping path layout and etc. It is indicated that the exploitation of the remanufacturing system will provide an effective way for the remanufacturing of metal defective parts.

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Fan, Zhiqiang, Shanshan Li, and Zhijun Gao. "Multiobjective Sustainable Order Allocation Problem Optimization with Improved Genetic Algorithm Using Priority Encoding." Mathematical Problems in Engineering 2019 (November 26, 2019): 1–12. http://dx.doi.org/10.1155/2019/8218709.

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Recently, incorporating carbon emissions into order allocation decisions has attracted considerable attention among scholars and industrialists. Moreover, affected by the random fluctuations of the man, machine, material, method, and environment (4M1E), the production process is usually imperfect with defective products. Reducing product defective rates can effectively improve the quality of the order allocation process. Therefore, considering product defective rate and carbon emission, a multiobjective integer nonlinear programming (INLP) formulation is presented to address this multiproduct, multiperiod, and multi-OEM order allocation problem. Furthermore, exploring the existing literatures, an improved genetic algorithm using priority encoding (IGAUPE) is put forward as a novel optimization technique. Finally, numerical experiments are conducted to validate the correctness of the proposed INLP model as well as the effectiveness of the proposed algorithm. Compared with the genetic algorithm using binary encoding (GAUBE), genetic algorithm using two-layer encoding (GAUTE), and LINGO software, the experiment results show that IGAUPE can improve the efficiency and effectiveness within the predetermined time limit when solving large-scale instances.

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Jindam, Sowjanya, Sai Teja Challa, Sai Jahnavi Chada, Navya Sree B, B, and Srinidhi Malgireddy. "Prediction of Software Defects using Ensemble Machine Learning Techniques." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 5 (January 30, 2023): 58–65. http://dx.doi.org/10.35940/ijrte.e7421.0111523.

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During software development and maintenance, predicting software bugs becomes critical. Defect prediction early in the software development life cycle is an important aspect of the quality assurance process that has received a lot of attention in the previous two decades. Early detection of defective modules in software development can support the development team in efficiently and effectively utilizing available resources to provide high-quality software products in a short amount of time. The machine learning approach, which works by detecting hidden patterns among software features, is an excellent way to identify problematic modules. The software flaws in NASA datasets MC1, MW1, KC3, and PC4 are predicted using multiple machine learning classification algorithms in this work. A new model was developed based on altering the parameters of the previous XGBoost model, including N_estimator, learning rate, max depth, and subsample. The results were compared to those obtained by state-of-the-art models, and our model outperformed them across all datasets.

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Zheng, Shang, Jinjing Gai, Hualong Yu, Haitao Zou, and Shang Gao. "Software Defect Prediction Based on Fuzzy Weighted Extreme Learning Machine with Relative Density Information." Scientific Programming 2020 (November 18, 2020): 1–18. http://dx.doi.org/10.1155/2020/8852705.

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To identify software modules that are more likely to be defective, machine learning has been used to construct software defect prediction (SDP) models. However, several previous works have found that the imbalanced nature of software defective data can decrease the model performance. In this paper, we discussed the issue of how to improve imbalanced data distribution in the context of SDP, which can benefit software defect prediction with the aim of finding better methods. Firstly, a relative density was introduced to reflect the significance of each instance within its class, which is irrelevant to the scale of data distribution in feature space; hence, it can be more robust than the absolute distance information. Secondly, a K-nearest-neighbors-based probability density estimation (KNN-PDE) alike strategy was utilised to calculate the relative density of each training instance. Furthermore, the fuzzy memberships of sample were designed based on relative density in order to eliminate classification error coming from noise and outlier samples. Finally, two algorithms were proposed to train software defect prediction models based on the weighted extreme learning machine. This paper compared the proposed algorithms with traditional SDP methods on the benchmark data sets. It was proved that the proposed methods have much better overall performance in terms of the measures including G-mean, AUC, and Balance. The proposed algorithms are more robust and adaptive for SDP data distribution types and can more accurately estimate the significance of each instance and assign the identical total fuzzy coefficients for two different classes without considering the impact of data scale.

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Khan, Bilal, Rashid Naseem, Muhammad Arif Shah, Karzan Wakil, Atif Khan, M. Irfan Uddin, and Marwan Mahmoud. "Software Defect Prediction for Healthcare Big Data: An Empirical Evaluation of Machine Learning Techniques." Journal of Healthcare Engineering 2021 (March 15, 2021): 1–16. http://dx.doi.org/10.1155/2021/8899263.

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Software defect prediction (SDP) in the initial period of the software development life cycle (SDLC) remains a critical and important assignment. SDP is essentially studied during few last decades as it leads to assure the quality of software systems. The quick forecast of defective or imperfect artifacts in software development may serve the development team to use the existing assets competently and more effectively to provide extraordinary software products in the given or narrow time. Previously, several canvassers have industrialized models for defect prediction utilizing machine learning (ML) and statistical techniques. ML methods are considered as an operative and operational approach to pinpoint the defective modules, in which moving parts through mining concealed patterns amid software metrics (attributes). ML techniques are also utilized by several researchers on healthcare datasets. This study utilizes different ML techniques software defect prediction using seven broadly used datasets. The ML techniques include the multilayer perceptron (MLP), support vector machine (SVM), decision tree (J48), radial basis function (RBF), random forest (RF), hidden Markov model (HMM), credal decision tree (CDT), K-nearest neighbor (KNN), average one dependency estimator (A1DE), and Naïve Bayes (NB). The performance of each technique is evaluated using different measures, for instance, relative absolute error (RAE), mean absolute error (MAE), root mean squared error (RMSE), root relative squared error (RRSE), recall, and accuracy. The inclusive outcome shows the best performance of RF with 88.32% average accuracy and 2.96 rank value, second-best performance is achieved by SVM with 87.99% average accuracy and 3.83 rank values. Moreover, CDT also shows 87.88% average accuracy and 3.62 rank values, placed on the third position. The comprehensive outcomes of research can be utilized as a reference point for new research in the SDP domain, and therefore, any assertion concerning the enhancement in prediction over any new technique or model can be benchmarked and proved.

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Kabir, Md Alamgir, Shahina Begum, Mobyen Uddin Ahmed, and Atiq Ur Rehman. "CODE: A Moving-Window-Based Framework for Detecting Concept Drift in Software Defect Prediction." Symmetry 14, no. 12 (November 28, 2022): 2508. http://dx.doi.org/10.3390/sym14122508.

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Concept drift (CD) refers to data distributions that may vary after a minimum stable period. CD negatively influences models’ performance of software defect prediction (SDP) trained on past datasets when applied to the new datasets. Based on previous studies of SDP, it is confirmed that the accuracy of prediction models is negatively affected due to changes in data distributions. Moreover, cross-version (CV) defect data are naturally asymmetric due to the nature of their class imbalance. In this paper, a moving window-based concept-drift detection (CODE) framework is proposed to detect CD in chronologically asymmetric defective datasets and to investigate the feasibility of alleviating CD from the data. The proposed CODE framework consists of four steps, in which the first pre-processes the defect datasets and forms CV chronological data, the second constructs the CV defect models, the third calculates the test statistics, and the fourth provides a hypothesis-test-based CD detection method. In prior studies of SDP, it is observed that in an effort to make the data more symmetric, class-rebalancing techniques are utilized, and this improves the prediction performance of the models. The ability of the CODE framework is demonstrated by conducting experiments on 36 versions of 10 software projects. Some of the key findings are: (1) Up to 50% of the chronological-defect datasets are drift-prone while applying the most popular classifiers used from the SDP literature. (2) The class-rebalancing techniques had a positive impact on the prediction performance for CVDP by correctly classifying the CV defective modules and detected CD by up to 31% on the resampled datasets.

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Lokavee, Shongpun, Chatchawal Wongchoosuk, and Teerakiat Kerdcharoen. "Molecular Dynamics Simulation of Bi-Carboxyl Sidewall Functionalized Single-Wall Carbon Nanotubes in Water." Advanced Materials Research 1131 (December 2015): 106–9. http://dx.doi.org/10.4028/www.scientific.net/amr.1131.106.

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Functionalized single-walled carbon nanotubes (f-SWNTs) have attracted great interest due to their enhancement of SWNT properties leading to an increase in potential applications beyond those of pristine SWNT. In this work, we have investigated the behavior of open-end (9,0) bi-carboxyl sidewall functionalized SWNTs in water using molecular dynamics (MD) technique within GROMACS software package based on the OPLS force fields with modified charges obtained from the first principles calculations. The model tubes including perfect and defective nanotubes covalently functionalized by bi-carboxylic groups on different sidewall surface orientation were fully optimized by B3LYP/6-31G(d,p). The simulations were performed at the constant volume and temperature in a rectangular box with periodic boundary conditions in which each system contains one model tube and ~1680 water molecules. The results form MD simulations showed that functionalization on the central carbon atom in the (C1,C ́1)SW-defective sites strongly affects on the dynamic behavior of CNT in water. Results showed that the hydrophilic behavior of the functionalized SWNT has been improved over the pristine and defective nanotubes.

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Naseem, Rashid, Bilal Khan, Arshad Ahmad, Ahmad Almogren, Saima Jabeen, Bashir Hayat, and Muhammad Arif Shah. "Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects." Complexity 2020 (November 30, 2020): 1–21. http://dx.doi.org/10.1155/2020/6688075.

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Software defects prediction at the initial period of the software development life cycle remains a critical and important assignment. Defect prediction and correctness leads to the assurance of the quality of software systems and has remained integral to study in the previous years. The quick forecast of imperfect or defective modules in software development can serve the development squad to use the existing assets competently and effectively to provide remarkable software products in a given short timeline. Hitherto, several researchers have industrialized defect prediction models by utilizing statistical and machine learning techniques that are operative and effective approaches to pinpoint the defective modules. Tree family machine learning techniques are well-thought-out to be one of the finest and ordinarily used supervised learning methods. In this study, different tree family machine learning techniques are employed for software defect prediction using ten benchmark datasets. These techniques include Credal Decision Tree (CDT), Cost-Sensitive Decision Forest (CS-Forest), Decision Stump (DS), Forest by Penalizing Attributes (Forest-PA), Hoeffding Tree (HT), Decision Tree (J48), Logistic Model Tree (LMT), Random Forest (RF), Random Tree (RT), and REP-Tree (REP-T). Performance of each technique is evaluated using different measures, i.e., mean absolute error (MAE), relative absolute error (RAE), root mean squared error (RMSE), root relative squared error (RRSE), specificity, precision, recall, F-measure (FM), G-measure (GM), Matthew’s correlation coefficient (MCC), and accuracy. The overall outcomes of this paper suggested RF technique by producing best results in terms of reducing error rates as well as increasing accuracy on five datasets, i.e., AR3, PC1, PC2, PC3, and PC4. The average accuracy achieved by RF is 90.2238%. The comprehensive outcomes of this study can be used as a reference point for other researchers. Any assertion concerning the enhancement in prediction through any new model, technique, or framework can be benchmarked and verified.

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Mohammed, Khwaja Muinuddin Chisti, Srinivas Kumar S, and Prasad G. "Defective texture classification using optimized neural network structure." Pattern Recognition Letters 135 (July 2020): 228–36. http://dx.doi.org/10.1016/j.patrec.2020.04.017.

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Journal articles on the topic 'Defective software'

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