Zaloguj się

Gotowe bibliografie tematyczne / SOFTWARE PREDICTION MODELS / Artykuły w czasopismach

Kliknij ten link, aby zobaczyć inne rodzaje publikacji na ten temat: SOFTWARE PREDICTION MODELS.

Artykuły w czasopismach na temat „SOFTWARE PREDICTION MODELS”

Autor: Grafiati

Data publikacji: 11 września 2023

Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych

Wybierz rodzaj źródła:

Sprawdź 50 najlepszych artykułów w czasopismach naukowych na temat „SOFTWARE PREDICTION MODELS”.

Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.

Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.

Przeglądaj artykuły w czasopismach z różnych dziedzin i twórz odpowiednie bibliografie.

1

Balogun, A. O., A. O. Bajeh, H. A. Mojeed, and A. G. Akintola. "Software defect prediction: A multi-criteria decision-making approach." Nigerian Journal of Technological Research 15, no. 1 (2020): 35–42. http://dx.doi.org/10.4314/njtr.v15i1.7.

Pełny tekst źródła

Streszczenie:

Failure of software systems as a result of software testing is very much rampant as modern software systems are large and complex. Software testing which is an integral part of the software development life cycle (SDLC), consumes both human and capital resources. As such, software defect prediction (SDP) mechanisms are deployed to strengthen the software testing phase in SDLC by predicting defect prone modules or components in software systems. Machine learning models are used for developing the SDP models with great successes achieved. Moreover, some studies have highlighted that a combinatio

Style APA, Harvard, Vancouver, ISO itp.

2

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 (2021): 193–215. http://dx.doi.org/10.1142/s0218194021500054.

Pełny tekst źródła

Streszczenie:

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 de

Style APA, Harvard, Vancouver, ISO itp.

3

Vandecruys, Olivier, David Martens, Bart Baesens, Christophe Mues, Manu De Backer, and Raf Haesen. "Mining software repositories for comprehensible software fault prediction models." Journal of Systems and Software 81, no. 5 (2008): 823–39. http://dx.doi.org/10.1016/j.jss.2007.07.034.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

4

Zaim, Amirul, Johanna Ahmad, Noor Hidayah Zakaria, Goh Eg Su, and Hidra Amnur. "Software Defect Prediction Framework Using Hybrid Software Metric." JOIV : International Journal on Informatics Visualization 6, no. 4 (2022): 921. http://dx.doi.org/10.30630/joiv.6.4.1258.

Pełny tekst źródła

Streszczenie:

Software fault prediction is widely used in the software development industry. Moreover, software development has accelerated significantly during this epidemic. However, the main problem is that most fault prediction models disregard object-oriented metrics, and even academician researcher concentrate on predicting software problems early in the development process. This research highlights a procedure that includes an object-oriented metric to predict the software fault at the class level and feature selection techniques to assess the effectiveness of the machine learning algorithm to predic

Style APA, Harvard, Vancouver, ISO itp.

5

Kalouptsoglou, Ilias, Miltiadis Siavvas, Dionysios Kehagias, Alexandros Chatzigeorgiou, and Apostolos Ampatzoglou. "Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction." Entropy 24, no. 5 (2022): 651. http://dx.doi.org/10.3390/e24050651.

Pełny tekst źródła

Streszczenie:

Software security is a very important aspect for software development organizations who wish to provide high-quality and dependable software to their consumers. A crucial part of software security is the early detection of software vulnerabilities. Vulnerability prediction is a mechanism that facilitates the identification (and, in turn, the mitigation) of vulnerabilities early enough during the software development cycle. The scientific community has recently focused a lot of attention on developing Deep Learning models using text mining techniques for predicting the existence of vulnerabilit

Style APA, Harvard, Vancouver, ISO itp.

6

Shatnawi, Raed. "Software fault prediction using machine learning techniques with metric thresholds." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 2 (2021): 159–72. http://dx.doi.org/10.3233/kes-210061.

Pełny tekst źródła

Streszczenie:

BACKGROUND: Fault data is vital to predicting the fault-proneness in large systems. Predicting faulty classes helps in allocating the appropriate testing resources for future releases. However, current fault data face challenges such as unlabeled instances and data imbalance. These challenges degrade the performance of the prediction models. Data imbalance happens because the majority of classes are labeled as not faulty whereas the minority of classes are labeled as faulty. AIM: The research proposes to improve fault prediction using software metrics in combination with threshold values. Stat

Style APA, Harvard, Vancouver, ISO itp.

7

Eldho, K. J. "Impact of Unbalanced Classification on the Performance of Software Defect Prediction Models." Indian Journal of Science and Technology 15, no. 6 (2022): 237–42. http://dx.doi.org/10.17485/ijst/v15i6.2193.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

8

Karunanithi, N., D. Whitley, and Y. K. Malaiya. "Prediction of software reliability using connectionist models." IEEE Transactions on Software Engineering 18, no. 7 (1992): 563–74. http://dx.doi.org/10.1109/32.148475.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

9

Fenton, N. E., and M. Neil. "A critique of software defect prediction models." IEEE Transactions on Software Engineering 25, no. 5 (1999): 675–89. http://dx.doi.org/10.1109/32.815326.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

10

Lawson, John S., Craig W. Wesselman, and Del T. Scott. "Simple Plots Improve Software Reliability Prediction Models." Quality Engineering 15, no. 3 (2003): 411–17. http://dx.doi.org/10.1081/qen-120018040.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

11

Radliński, Łukasz. "The Impact of Data Quality on Software Testing Effort Prediction." Electronics 12, no. 7 (2023): 1656. http://dx.doi.org/10.3390/electronics12071656.

Pełny tekst źródła

Streszczenie:

Background: This paper investigates the impact of data quality on the performance of models predicting effort on software testing. Data quality was reflected by training data filtering strategies (data variants) covering combinations of Data Quality Rating, UFP Rating, and a threshold of valid cases. Methods: The experiment used the ISBSG dataset and 16 machine learning models. A process of three-fold cross-validation repeated 20 times was used to train and evaluate each model with each data variant. Model performance was assessed using absolute errors of prediction. A ‘win–tie–loss’ procedure

Style APA, Harvard, Vancouver, ISO itp.

12

GANESAN, K., TAGHI M. KHOSHGOFTAAR, and EDWARD B. ALLEN. "CASE-BASED SOFTWARE QUALITY PREDICTION." International Journal of Software Engineering and Knowledge Engineering 10, no. 02 (2000): 139–52. http://dx.doi.org/10.1142/s0218194000000092.

Pełny tekst źródła

Streszczenie:

Highly reliable software is becoming an essential ingredient in many systems. However, assuring reliability often entails time-consuming costly development processes. One cost-effective strategy is to target reliability-enhancement activities to those modules that are likely to have the most problems. Software quality prediction models can predict the number of faults expected in each module early enough for reliability enhancement to be effective. This paper introduces a case-based reasoning technique for the prediction of software quality factors. Case-based reasoning is a technique that see

Style APA, Harvard, Vancouver, ISO itp.

13

Alsolai, Hadeel, and Marc Roper. "The Impact of Ensemble Techniques on Software Maintenance Change Prediction: An Empirical Study." Applied Sciences 12, no. 10 (2022): 5234. http://dx.doi.org/10.3390/app12105234.

Pełny tekst źródła

Streszczenie:

Various prediction models have been proposed by researchers to predict the change-proneness of classes based on source code metrics. However, some of these models suffer from low prediction accuracy because datasets exhibit high dimensionality or imbalanced classes. Recent studies suggest that using ensembles to integrate several models, select features, or perform sampling has the potential to resolve issues in the datasets and improve the prediction accuracy. This study aims to empirically evaluate the effectiveness of the ensemble models, feature selection, and sampling techniques on predic

Style APA, Harvard, Vancouver, ISO itp.

14

Yang, Xinli, Jingjing Liu, and Denghui Zhang. "A Comprehensive Taxonomy for Prediction Models in Software Engineering." Information 14, no. 2 (2023): 111. http://dx.doi.org/10.3390/info14020111.

Pełny tekst źródła

Streszczenie:

Applying prediction models to software engineering is an interesting research area. There have been many related studies which leverage prediction models to achieve good performance in various software engineering tasks. With more and more researches in software engineering leverage prediction models, there is a need to sort out related studies, aiming to summarize which software engineering tasks prediction models can apply to and how to better leverage prediction models in these tasks. This article conducts a comprehensive taxonomy on prediction models applied to software engineering. We rev

Style APA, Harvard, Vancouver, ISO itp.

15

CHALLAGULLA, VENKATA UDAYA B., FAROKH B. BASTANI, I.-LING YEN, and RAYMOND A. PAUL. "EMPIRICAL ASSESSMENT OF MACHINE LEARNING BASED SOFTWARE DEFECT PREDICTION TECHNIQUES." International Journal on Artificial Intelligence Tools 17, no. 02 (2008): 389–400. http://dx.doi.org/10.1142/s0218213008003947.

Pełny tekst źródła

Streszczenie:

Automated reliability assessment is essential for systems that entail dynamic adaptation based on runtime mission-specific requirements. One approach along this direction is to monitor and assess the system using machine learning-based software defect prediction techniques. Due to the dynamic nature of software data collected, Instance-based learning algorithms are proposed for the above purposes. To evaluate the accuracy of these methods, the paper presents an empirical analysis of four different real-time software defect data sets using different predictor models. The results show that a com

Style APA, Harvard, Vancouver, ISO itp.

16

John, Boby. "A Brief Review of Software Reliability Prediction Models." International Journal for Research in Applied Science and Engineering Technology V, no. IV (2017): 990–97. http://dx.doi.org/10.22214/ijraset.2017.4180.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

17

Schneidewind, Norman. "Experience with Risk-Based Software Defect Prediction Models." Journal of Aerospace Computing, Information, and Communication 4, no. 1 (2007): 619–27. http://dx.doi.org/10.2514/1.26507.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

18

Koru, A. G., and Hongfang Liu. "Building Defect Prediction Models in Practice." IEEE Software 22, no. 6 (2005): 23–29. http://dx.doi.org/10.1109/ms.2005.149.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

19

Jiang, Yue, Bojan Cukic, and Yan Ma. "Techniques for evaluating fault prediction models." Empirical Software Engineering 13, no. 5 (2008): 561–95. http://dx.doi.org/10.1007/s10664-008-9079-3.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

20

Malhotra, Ruchika, and Juhi Jain. "Predicting defects in imbalanced data using resampling methods: an empirical investigation." PeerJ Computer Science 8 (April 29, 2022): e573. http://dx.doi.org/10.7717/peerj-cs.573.

Pełny tekst źródła

Streszczenie:

The development of correct and effective software defect prediction (SDP) models is one of the utmost needs of the software industry. Statistics of many defect-related open-source data sets depict the class imbalance problem in object-oriented projects. Models trained on imbalanced data leads to inaccurate future predictions owing to biased learning and ineffective defect prediction. In addition to this large number of software metrics degrades the model performance. This study aims at (1) identification of useful metrics in the software using correlation feature selection, (2) extensive compa

Style APA, Harvard, Vancouver, ISO itp.

21

Ma, Baojun, Huaping Zhang, Guoqing Chen, Yanping Zhao, and Bart Baesens. "Investigating Associative Classification for Software Fault Prediction: An Experimental Perspective." International Journal of Software Engineering and Knowledge Engineering 24, no. 01 (2014): 61–90. http://dx.doi.org/10.1142/s021819401450003x.

Pełny tekst źródła

Streszczenie:

It is a recurrent finding that software development is often troubled by considerable delays as well as budget overruns and several solutions have been proposed in answer to this observation, software fault prediction being a prime example. Drawing upon machine learning techniques, software fault prediction tries to identify upfront software modules that are most likely to contain faults, thereby streamlining testing efforts and improving overall software quality. When deploying fault prediction models in a production environment, both prediction performance and model comprehensibility are typ

Style APA, Harvard, Vancouver, ISO itp.

22

Mariño, Perfecto, Francisco Poza, Santiago Otero, and Fernando Machado. "Multidisciplinary Software Developments in a Power Transformers Scenario." Key Engineering Materials 293-294 (September 2005): 635–42. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.635.

Pełny tekst źródła

Streszczenie:

Power transformers’ failures carry great costs to electric companies. To diminish this problem in four working 40 MVA transformers, the authors have implemented the measurement system of a failure prediction tool, which is the basis of a predictive maintenance infrastructure. The prediction models obtain their inputs from sensors, whose values must be conditioned, sampled and filtered before feeding the forecasting algorithms. Applying Data Warehouse tech- niques, the models have been provided with an abstraction of sensors the authors have called Virtual Cards. By means of these virtual devic

Style APA, Harvard, Vancouver, ISO itp.

23

Kumar, Ajay, and Kamaldeep Kaur. "SOM-FTS: A Hybrid Model for Software Reliability Prediction and MCDM-Based Evaluation." International Journal of Engineering and Technology Innovation 12, no. 4 (2022): 308–21. http://dx.doi.org/10.46604/ijeti.2022.8546.

Pełny tekst źródła

Streszczenie:

The objective of this study is to propose a hybrid model based on self-organized maps (SOM) and fuzzy time series (FTS) for predicting the reliability of software systems. The proposed SOM-FTS model is compared with eleven traditional machine learning-based models. The problem of selecting a suitable software reliability prediction model is represented as a multi-criteria decision-making (MCDM) problem. Twelve software reliability prediction models, including the proposed SOM-FTS model, are evaluated using three MCDM methods, four performance measures, and three software failure datasets. The

Style APA, Harvard, Vancouver, ISO itp.

24

Canaparo, Marco, and Elisabetta Ronchieri. "Data Mining Techniques for Software Quality Prediction in Open Source Software." EPJ Web of Conferences 214 (2019): 05007. http://dx.doi.org/10.1051/epjconf/201921405007.

Pełny tekst źródła

Streszczenie:

Software quality monitoring and analysis are among the most productive topics in software engineering research. Their results may be effectively employed by engineers during software development life cycle. Open source software constitutes a valid test case for the assessment of software characteristics. The data mining approach has been proposed in literature to extract software characteristics from software engineering data. This paper aims at comparing diverse data mining techniques (e.g., derived from machine learning) for developing effective software quality prediction models. To achieve

Style APA, Harvard, Vancouver, ISO itp.

25

JIANG, YUE, BOJAN CUKIC, TIM MENZIES, and JIE LIN. "INCREMENTAL DEVELOPMENT OF FAULT PREDICTION MODELS." International Journal of Software Engineering and Knowledge Engineering 23, no. 10 (2013): 1399–425. http://dx.doi.org/10.1142/s0218194013500447.

Pełny tekst źródła

Streszczenie:

The identification of fault-prone modules has a significant impact on software quality assurance. In addition to prediction accuracy, one of the most important goals is to detect fault prone modules as early as possible in the development lifecycle. Requirements, design, and code metrics have been successfully used for predicting fault-prone modules. In this paper, we investigate the benefits of the incremental development of software fault prediction models. We compare the performance of these models as the volume of data and their life cycle origin (design, code, or their combination) evolve

Style APA, Harvard, Vancouver, ISO itp.

26

SCHNEIDEWIND, NORMAN. "SOFTWARE RISK ANALYSIS." International Journal of Reliability, Quality and Safety Engineering 16, no. 02 (2009): 117–36. http://dx.doi.org/10.1142/s0218539309003320.

Pełny tekst źródła

Streszczenie:

There has been a lack of attention to the subject of risk management in the design and operation of software. This is strange because the risk to reliability is a critical problem in attempts to achieve a safe operation of the software. To address this problem, we evaluate existing models and introduce a new model for software risk prediction. The new model — cumulative failures gradient function — is based on the principles of neural networks. This metric identifiers the minimum test time required to achieve maximum improvement in software quality. We used three NASA Space Shuttle software sy

Style APA, Harvard, Vancouver, ISO itp.

27

Islam, Mohammad Rubyet, and Peter Sandborn. "Demonstration of a Response Time Based Remaining Useful Life (RUL) Prediction for Software Systems." Journal of Prognostics and Health Management 3, no. 1 (2023): 9–36. http://dx.doi.org/10.22215/jphm.v3i1.3641.

Pełny tekst źródła

Streszczenie:

Prognostic and Health Management (PHM) has been widely applied to hardware systems in the electronics and non-electronics domains but has not been explored for software. While software does not decay over time, it can degrade over release cycles. Software health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental. Relevant research areas such as software defect prediction, software reliability prediction, predictive maintenance of software, software degradation, and sof

Style APA, Harvard, Vancouver, ISO itp.

28

Gradišnik, Mitja, Tina Beranič, and Sašo Karakatič. "Impact of Historical Software Metric Changes in Predicting Future Maintainability Trends in Open-Source Software Development." Applied Sciences 10, no. 13 (2020): 4624. http://dx.doi.org/10.3390/app10134624.

Pełny tekst źródła

Streszczenie:

Software maintenance is one of the key stages in the software lifecycle and it includes a variety of activities that consume the significant portion of the costs of a software project. Previous research suggest that future software maintainability can be predicted, based on various source code aspects, but most of the research focuses on the prediction based on the present state of the code and ignores its history. While taking the history into account in software maintainability prediction seems intuitive, the research empirically testing this has not been done, and is the main goal of this p

Style APA, Harvard, Vancouver, ISO itp.

29

Timonidis, Nestor, Rembrandt Bakker, and Paul Tiesinga. "Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data." Neuroinformatics 18, no. 4 (2020): 611–26. http://dx.doi.org/10.1007/s12021-020-09471-x.

Pełny tekst źródła

Streszczenie:

Abstract Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection patterns and assessed the functional annotations of the most predictive groups of genes. To achieve our goal we used publicly available volumetric gene expression and connectivity data

Style APA, Harvard, Vancouver, ISO itp.

30

Siswantoro, Muhammad Zain Fawwaz Nuruddin, and Umi Laili Yuhana. "Software Defect Prediction Based on Optimized Machine Learning Models: A Comparative Study." Teknika 12, no. 2 (2023): 166–72. http://dx.doi.org/10.34148/teknika.v12i2.634.

Pełny tekst źródła

Streszczenie:

Software defect prediction is crucial used for detecting possible defects in software before they manifest. While machine learning models have become more prevalent in software defect prediction, their effectiveness may vary based on the dataset and hyperparameters of the model. Difficulties arise in determining the most suitable hyperparameters for the model, as well as identifying the prominent features that serve as input to the classifier. This research aims to evaluate various traditional machine learning models that are optimized for software defect prediction on NASA MDP (Metrics Data P

Style APA, Harvard, Vancouver, ISO itp.

31

Zighed, Narimane, Nora Bounour, and Abdelhak-Djamel Seriai. "Comparative Analysis of Object-Oriented Software Maintainability Prediction Models." Foundations of Computing and Decision Sciences 43, no. 4 (2018): 359–74. http://dx.doi.org/10.1515/fcds-2018-0018.

Pełny tekst źródła

Streszczenie:

Abstract Software maintainability is one of the most important aspects when evaluating the quality of a software product. It is defined as the ease with which the existing software can be modified. In the literature, several researchers have proposed a large number of models to measure and predict maintainability throughout different phases of the Software Development Life Cycle. However, only a few attempts have been made for conducting a comparative study of the existent proposed prediction models. In this paper, we present a detailed classification and conduct a comparative analysis of Obje

Style APA, Harvard, Vancouver, ISO itp.

32

Diwan, Sinan, and Abdul Syukor Mohamad. "Machine Learning Empowered Software Prediction System." Wasit Journal of Computer and Mathematics Science 1, no. 3 (2022): 54–64. http://dx.doi.org/10.31185/wjcm.61.

Pełny tekst źródła

Streszczenie:

Prediction of software defects is one of the most active study fields in software engineering today. Using a defect prediction model, a list of code prone to defects may be compiled. Using a defect prediction model, software may be made more reliable by identifying and discovering faults before or during the software enhancement process. Defect prediction will play an increasingly important role in the design process as the scope of software projects grows. Bugs or the number of bugs used to measure the performance of a defect prediction procedure are referred to as "bugs" in this context. Def

Style APA, Harvard, Vancouver, ISO itp.

33

Desai, Bhoushika, and Roopesh Kevin Sungkur. "Software Quality Prediction Using Machine Learning." International Journal of Software Innovation 10, no. 1 (2022): 1–35. http://dx.doi.org/10.4018/ijsi.297997.

Pełny tekst źródła

Streszczenie:

With the emergence of Machine Learning, many companies are increasingly embracing this revolutionary approach, both in terms of growth and maintenance, to reduce software costs. This research aimed at building two models which is Software Defect Prediction Model (SDPM) which will be used to predict defects in software and Software Maintainability Prediction Model (SMPM) which will be used for Software Maintainability. Different classifiers, namely Random Forest, Decision Tree, Naïve Bayes and Artificial Neural Networks have been considered and then evaluated using different metrics such as Acc

Style APA, Harvard, Vancouver, ISO itp.

34

Hong, Euy-Seok. "Taxonomy Framework for Metric-based Software Quality Prediction Models." Journal of the Korea Contents Association 10, no. 6 (2010): 134–43. http://dx.doi.org/10.5392/jkca.2010.10.6.134.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

35

MURAKAMI, Yukasa, Masateru TSUNODA, and Koji TODA. "Evaluation of Software Fault Prediction Models Considering Faultless Cases." IEICE Transactions on Information and Systems E103.D, no. 6 (2020): 1319–27. http://dx.doi.org/10.1587/transinf.2019kbp0019.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

36

Chamoli, Shilpee, Gil Tenne, and Sanjay Bhatia. "Analysing Software Metrics for Accurate Dynamic Defect Prediction Models." Indian Journal of Science and Technology 8, S4 (2015): 96. http://dx.doi.org/10.17485/ijst/2015/v8is4/63111.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

37

Syeed, M. M. Mahbubul, Imed Hammouda, and Tarja Systä. "Prediction Models and Techniques for Open Source Software Projects." International Journal of Open Source Software and Processes 5, no. 2 (2014): 1–39. http://dx.doi.org/10.4018/ijossp.2014040101.

Pełny tekst źródła

Streszczenie:

Open Source Software (OSS) is currently a widely adopted approach to developing and distributing software. For effective adoption of OSS, fundamental knowledge of project development is needed. This often calls for reliable prediction models to simulate project evolution and to envision project future. These models provide help in supporting preventive maintenance and building quality software. This paper reports on a systematic literature survey aimed at the identification and structuring of research that offer prediction models and techniques in analyzing OSS projects. In this review, we sys

Style APA, Harvard, Vancouver, ISO itp.

38

Mahesha, Pandit, and Gupta Deepali. "Performance of Genetic Programming-based Software Defect Prediction Models." International Journal of Performability Engineering 17, no. 9 (2021): 787. http://dx.doi.org/10.23940/ijpe.21.09.p5.787795.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

39

LaMotte, Lynn R., and Jeffrey D. Wells. "Inverse prediction for heteroscedastic response using mixed models software." Communications in Statistics - Simulation and Computation 46, no. 6 (2017): 4490–98. http://dx.doi.org/10.1080/03610918.2015.1118508.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

40

Miyazaki, Y., A. Takanou, H. Nozaki, N. Nakagawa, and K. Okada. "Method to estimate parameter values in software prediction models." Information and Software Technology 33, no. 3 (1991): 239–43. http://dx.doi.org/10.1016/0950-5849(91)90139-3.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

41

LaMotte, Lynn R., and Jeffrey D. Wells. "Inverse prediction for multivariate mixed models with standard software." Statistical Papers 57, no. 4 (2016): 929–38. http://dx.doi.org/10.1007/s00362-016-0815-2.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

42

Balogun, Abdullateef Oluwagbemiga, Shuib Basri, Said Jadid Abdulkadir, and Ahmad Sobri Hashim. "Performance Analysis of Feature Selection Methods in Software Defect Prediction: A Search Method Approach." Applied Sciences 9, no. 13 (2019): 2764. http://dx.doi.org/10.3390/app9132764.

Pełny tekst źródła

Streszczenie:

Software Defect Prediction (SDP) models are built using software metrics derived from software systems. The quality of SDP models depends largely on the quality of software metrics (dataset) used to build the SDP models. High dimensionality is one of the data quality problems that affect the performance of SDP models. Feature selection (FS) is a proven method for addressing the dimensionality problem. However, the choice of FS method for SDP is still a problem, as most of the empirical studies on FS methods for SDP produce contradictory and inconsistent quality outcomes. Those FS methods behav

Style APA, Harvard, Vancouver, ISO itp.

43

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 (2019): 121–26. http://dx.doi.org/10.14710/jtsiskom.7.4.2019.121-126.

Pełny tekst źródła

Streszczenie:

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 di

Style APA, Harvard, Vancouver, ISO itp.

44

Goyal, Somya, and Pradeep Kumar Bhatia. "Comparison of Machine Learning Techniques for Software Quality Prediction." International Journal of Knowledge and Systems Science 11, no. 2 (2020): 20–40. http://dx.doi.org/10.4018/ijkss.2020040102.

Pełny tekst źródła

Streszczenie:

Software quality prediction is one the most challenging tasks in the development and maintenance of software. Machine learning (ML) is widely being incorporated for the prediction of the quality of a final product in the early development stages of the software development life cycle (SDLC). An ML prediction model uses software metrics and faulty data from previous projects to detect high-risk modules for future projects, so that the testing efforts can be targeted to those specific ‘risky' modules. Hence, ML-based predictors contribute to the detection of development anomalies early and inexp

Style APA, Harvard, Vancouver, ISO itp.

45

Yanjun Li, Yanjun Li, Huan Huang Yanjun Li, Qiang Geng Huan Huang, Xinwei Guo Qiang Geng, and Yuyu Yuan Xinwei Guo. "Fairness Measures of Machine Learning Models in Judicial Penalty Prediction." 網際網路技術學刊 23, no. 5 (2022): 1109–16. http://dx.doi.org/10.53106/160792642022092305019.

Pełny tekst źródła

Streszczenie:

<p>Machine learning (ML) has been widely adopted in many software applications across domains. However, accompanying the outstanding performance, the behaviors of the ML models, which are essentially a kind of black-box software, could be unfair and hard to understand in many cases. In our human-centered society, an unfair decision could potentially damage human value, even causing severe social consequences, especially in decision-critical scenarios such as legal judgment. Although some existing works investigated the ML models in terms of robustness, accuracy, security, privacy, qualit

Style APA, Harvard, Vancouver, ISO itp.

46

Ali, Awad, Mohammed Bakri Bashir, Alzubair Hassan, et al. "Design-Time Reliability Prediction Model for Component-Based Software Systems." Sensors 22, no. 7 (2022): 2812. http://dx.doi.org/10.3390/s22072812.

Pełny tekst źródła

Streszczenie:

Software reliability is prioritised as the most critical quality attribute. Reliability prediction models participate in the prevention of software failures which can cause vital events and disastrous consequences in safety-critical applications or even in businesses. Predicting reliability during design allows software developers to avoid potential design problems, which can otherwise result in reconstructing an entire system when discovered at later stages of the software development life-cycle. Several reliability models have been built to predict reliability during software development. Ho

Style APA, Harvard, Vancouver, ISO itp.

47

Almayyan, Waheeda. "Towards Predicting Software Defects with Clustering Techniques." International Journal of Artificial Intelligence & Applications 12, no. 1 (2021): 39–54. http://dx.doi.org/10.5121/ijaia.2021.12103.

Pełny tekst źródła

Streszczenie:

The purpose of software defect prediction is to improve the quality of a software project by building a predictive model to decide whether a software module is or is not fault prone. In recent years, much research in using machine learning techniques in this topic has been performed. Our aim was to evaluate the performance of clustering techniques with feature selection schemes to address the problem of software defect prediction problem. We analysed the National Aeronautics and Space Administration (NASA) dataset benchmarks using three clustering algorithms: (1) Farthest First, (2) X-Means, a

Style APA, Harvard, Vancouver, ISO itp.

48

Yuan, Yuyu, Chenlong Li, and Jincui Yang. "An Improved Confounding Effect Model for Software Defect Prediction." Applied Sciences 13, no. 6 (2023): 3459. http://dx.doi.org/10.3390/app13063459.

Pełny tekst źródła

Streszczenie:

Software defect prediction technology can effectively improve software quality. Depending on the code metrics, machine learning models are built to predict potential defects. Some researchers have indicated that the size metric could cause confounding effects and bias the prediction results. However, evidence shows that the real confounder should be the development cycle and number of developers, which could bring confounding effects when using code metrics for prediction. This paper proposes an improved confounding effect model, introducing a new confounding variable into the traditional mode

Style APA, Harvard, Vancouver, ISO itp.

49

Kakkar, Misha, Sarika Jain, Abhay Bansal, and P. S. Grover. "Nonlinear Geometric Framework for Software Defect Prediction." International Journal of Decision Support System Technology 12, no. 3 (2020): 85–100. http://dx.doi.org/10.4018/ijdsst.2020070105.

Pełny tekst źródła

Streszczenie:

Humans use the software in every walk of life thus it is essential to have the best quality software. Software defect prediction models assist in identifying defect prone modules with the help of historical data, which in turn improves software quality. Historical data consists of data related to modules /files/classes which are labeled as buggy or clean. As the number of buggy artifacts as less as compared to clean artifacts, the nature of historical data becomes imbalance. Due to this uneven distribution of the data, it difficult for classification algorithms to build highly effective SDP mo

Style APA, Harvard, Vancouver, ISO itp.

50

Pan, Cong, Minyan Lu, and Biao Xu. "An Empirical Study on Software Defect Prediction Using CodeBERT Model." Applied Sciences 11, no. 11 (2021): 4793. http://dx.doi.org/10.3390/app11114793.

Pełny tekst źródła

Streszczenie:

Deep learning-based software defect prediction has been popular these days. Recently, the publishing of the CodeBERT model has made it possible to perform many software engineering tasks. We propose various CodeBERT models targeting software defect prediction, including CodeBERT-NT, CodeBERT-PS, CodeBERT-PK, and CodeBERT-PT. We perform empirical studies using such models in cross-version and cross-project software defect prediction to investigate if using a neural language model like CodeBERT could improve prediction performance. We also investigate the effects of different prediction patterns

Style APA, Harvard, Vancouver, ISO itp.

Oferujemy zniżki na wszystkie plany premium dla autorów, których prace zostały uwzględnione w tematycznych zestawieniach literatury. Skontaktuj się z nami, aby uzyskać unikalny kod promocyjny!