Relevant bibliographies by topics / Software defect density

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Software defect density'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Software defect density"

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

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It is very useful for improving software quality if we can find which software metrics are more correlative with software defects or defects density. Based on 33 actual software projects, we analyzed 44 software metrics from application level, file level, class level and function level, and do correlation analysis with the number of software defects and defect density, the results show that software metrics have little correlation with the number of software defects, but are correlative with defect density. Through correlation analysis, we selected five metrics that have larger correlation with defect density, these metrics can be used for improving software quality and predicting software defects density.
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Zhang, Wei, Zhen Yu Ma, Qing Ling Lu, Xiao Bing Nie, and Juan Liu. "Research on Software Defect Prediction Method Based on Machine Learning." Applied Mechanics and Materials 687-691 (November 2014): 2182–85. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.2182.

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This paper analyzed 44 metrics of application level, file level, class level and function level, and do correlation analysis with the number of software defects and defect density, the results show that software metrics have little correlation with the number of software defect, but are correlative with defect density. Through correlation analysis, we selected five metrics that have larger correlation with defect density. On the basis of feature selection, we predicted defect density with 16 machine learning models for 33 actual software projects. The results show that the Spearman rank correlation coefficient (SRCC) between the predicting defect density and the actual defect density based on SVR model is 0.6727, higher than other 15 machine learning models, the model that has the second absolute value of SRCC is IBk model, the SRCC only is-0.3557, the results show that the method based on SVR has the highest prediction accuracy.
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Verma, Dinesh, and Shishir Kumar. "An Improved Approach for Reduction of Defect Density Using Optimal Module Sizes." Advances in Software Engineering 2014 (August 24, 2014): 1–7. http://dx.doi.org/10.1155/2014/803530.

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Nowadays, software developers are facing challenges in minimizing the number of defects during the software development. Using defect density parameter, developers can identify the possibilities of improvements in the product. Since the total number of defects depends on module size, so there is need to calculate the optimal size of the module to minimize the defect density. In this paper, an improved model has been formulated that indicates the relationship between defect density and variable size of modules. This relationship could be used for optimization of overall defect density using an effective distribution of modules sizes. Three available data sets related to concern aspect have been examined with the proposed model by taking the distinct values of variables and parameter by putting some constraint on parameters. Curve fitting method has been used to obtain the size of module with minimum defect density. Goodness of fit measures has been performed to validate the proposed model for data sets. The defect density can be optimized by effective distribution of size of modules. The larger modules can be broken into smaller modules and smaller modules can be merged to minimize the overall defect density.
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SCHNEIDEWIND, NORMAN. "COMPLEXITY-DRIVEN RELIABILITY MODEL." International Journal of Reliability, Quality and Safety Engineering 15, no. 05 (2008): 479–94. http://dx.doi.org/10.1142/s0218539308003179.

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A model of software complexity and reliability is developed that uses an evolutionary process to transition from one software system to the next while complexity metrics are used to predict the reliability for each system. Systems are tested until the software passes defect presence criteria and is released. Testing criteria are based on defect count, defect density, and testing efficiency predictions exceeding specified thresholds. In addition, another type of testing efficiency — a directed graph representing the complexity of the software and defects embedded in the code — is used to evaluate the efficiency of defect detection in NASA satellite system software. Complexity metrics were found to be good predictors of defects and testing efficiency in this evolutionary process.
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Sherriff, Mark. "Utilizing verification and validation certificates to estimate software defect density." ACM SIGSOFT Software Engineering Notes 30, no. 5 (2005): 381–84. http://dx.doi.org/10.1145/1095430.1081768.

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Pipitone, J., and S. Easterbrook. "Assessing climate model software quality: a defect density analysis of three models." Geoscientific Model Development 5, no. 4 (2012): 1009–22. http://dx.doi.org/10.5194/gmd-5-1009-2012.

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Abstract. A climate model is an executable theory of the climate; the model encapsulates climatological theories in software so that they can be simulated and their implications investigated. Thus, in order to trust a climate model, one must trust that the software it is built from is built correctly. Our study explores the nature of software quality in the context of climate modelling. We performed an analysis of defect reports and defect fixes in several versions of leading global climate models by collecting defect data from bug tracking systems and version control repository comments. We found that the climate models all have very low defect densities compared to well-known, similarly sized open-source projects. We discuss the implications of our findings for the assessment of climate model software trustworthiness.
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Pipitone, J., and S. Easterbrook. "Assessing climate model software quality: a defect density analysis of three models." Geoscientific Model Development Discussions 5, no. 1 (2012): 347–82. http://dx.doi.org/10.5194/gmdd-5-347-2012.

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Abstract. A climate model is an executable theory of the climate; the model encapsulates climatological theories in software so that they can be simulated and their implications investigated. Thus, in order to trust a climate model one must trust that the software it is built from is built correctly. Our study explores the nature of software quality in the context of climate modelling. We performed an analysis of defect reports and defect fixes in several versions of leading global climate models by collecting defect data from bug tracking systems and version control repository comments. We found that the climate models all have very low defect densities compared to well-known, similarly sized open-source projects. We discuss the implications of our findings for the assessment of climate model software trustworthiness.
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Nugroho, Ariadi, and Michel R. V. Chaudron. "The impact of UML modeling on defect density and defect resolution time in a proprietary system." Empirical Software Engineering 19, no. 4 (2013): 926–54. http://dx.doi.org/10.1007/s10664-013-9243-2.

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Concas, Giulio, Michele Marchesi, Cristina Monni, Matteo Orrù, and Roberto Tonelli. "Software Quality and Community Structure in Java Software Networks." International Journal of Software Engineering and Knowledge Engineering 27, no. 07 (2017): 1063–96. http://dx.doi.org/10.1142/s0218194017500401.

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We present a study of 600 Java software networks with the aim of characterizing the relationship among their defectiveness and community metrics. We analyze the community structure of such networks, defined as their topological division into subnetworks of densely connected nodes. A high density of connections represents a higher level of cooperation between classes, so a well-defined division in communities could indicate that the software system has been designed in a modular fashion and all its functionalities are well separated. We show how the community structure can be an indicator of well-written, high quality code by retrieving the communities of the analyzed systems and by ranking their division in communities through the built-in metric called modularity. We found that the software systems with highest modularity possess the majority of bugs, and tested whether this result is related to some confounding effect. We found two power laws relating the maximum defect density with two different metrics: the number of detected communities inside a software network and the clustering coefficient. We finally found a linear correlation between clustering coefficient and number of communities. Our results can be used to make predictive hypotheses about software defectiveness of future releases of the analyzed systems.
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Amara, Dalila, Ezzeddine Fatnassi, and Latifa Ben Arfa Rabai. "An Empirical Assessment and Validation of Redundancy Metrics Using Defect Density as Reliability Indicator." Scientific Programming 2021 (February 19, 2021): 1–20. http://dx.doi.org/10.1155/2021/8325417.

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Software metrics which are language-dependent are proposed as quantitative measures to assess internal quality factors for both method and class levels like cohesion and complexity. The external quality factors like reliability and maintainability are in general predicted using different metrics of internal attributes. Literature review shows a lack of software metrics which are proposed for reliability measurement and prediction. In this context, a suite of four semantic language-independent metrics was proposed by Mili et al. (2014) to assess program redundancy using Shannon entropy measure. The main objective of these metrics is to monitor program reliability. Despite their important purpose, they are manually computed and only theoretically validated. Therefore, this paper aims to assess the redundancy metrics and empirically validate them as significant reliability indicators. As software reliability is an external attribute that cannot be directly evaluated, we employ other measurable quality factors that represent direct reflections of this attribute. Among these factors, defect density is widely used to measure and predict software reliability based on software metrics. Therefore, a linear regression technique is used to show the usefulness of these metrics as significant indicators of software defect density. A quantitative model is then proposed to predict software defect density based on redundancy metrics in order to monitor software reliability.
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Dissertations / Theses on the topic "Software defect density"

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Sargut, Kamil Umut. "Application Of Statistical Process Control To Software Development Processes Via Control Charts." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1270081/index.pdf.

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The application of Statistical Process Control (SPC) to software processes has been a challenging issue for software engineers and researchers. Although SPC is suggested for providing process control and achieving higher process maturity levels, there are very few resources that describe success stories, implementation details, and implemented guidelines for applying SPC to specific metrics. In this thesis the findings of a case study that is performed for investigating the applicability of SPC to software metrics in an emergent CMM Level 3 software organization are presented. As being one of the basic and most sophisticated tools of SPC, control charts are used for the analysis. The difficulties in application of Statistical Process Control to a CMM Level 3 organization are observed by using the existing data of defect density, rework percentage, productivity and review performance metrics and relevant suggestions are provided for dealing with them. Finally the analysis results are summarized and a guideline is prepared for software companies who want to utilize control charts by using their existing metric data.
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Nanchari, Nithin Krishna. "Austin Logistics Inc : assessing defect density." Thesis, 2010. http://hdl.handle.net/2152/ETD-UT-2010-12-2414.

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Austin Logistics Inc. Solutions provides tools that help centralize resource management, optimize and maintain compliance of calling schedules for consumer financial service organization (banks, financial institutions). With the increasing number of customers, the amount of rework and availability of resources had been notably decreasing over time; thereby negatively affecting the overall cost and quality of the software being delivered. The improvement objectives of the company and its departments were broadly stated but lacking a goal-driven nature. The software measurement Goal-Question-Metric (GQM) approach was chosen and used for this research initiative to better support business driven quality improvement. Software defect density data was collected and analyzed to identify significant deviations in the software development life cycle.. The results of the initial analysis on the transformed defect-tracking data helped identify the negatively affected areas within the software development life cycle. The data showed significant variations in the requirements, design and implementation phases of the product life cycle, thus helping identify various process improvement opportunities. On quantifying the change in defect density, the effectiveness of using GQM has also provided valuable insights for process improvement. Based on these results, we were able to identify some of the weaknesses and shortcomings in our application development process.<br>text
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Book chapters on the topic "Software defect density"

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Bahadur Yadav, Harikesh, and Dilip Kumar Yadav. "A Fuzzy Logic Approach for Multistage Defects Density Analysis of Software." In Advances in Intelligent Systems and Computing. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2220-0_10.

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Ravi Kumar, T., T. Srinivasa Rao, and Sandhya Bathini. "A Predictive Approach to Estimate Software Defects Density Using Weighted Artificial Neural Networks for the Given Software Metrics." In Smart Intelligent Computing and Applications. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1927-3_48.

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S., Sivakumar, Sreedevi E., PremaLatha V., and Haritha D. "Parallel Defect Detection Model on Uncertain Data for GPUs Computing by a Novel Ensemble Learning." In Applications of Artificial Intelligence for Smart Technology. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3335-2.ch010.

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To detect defect is an important concept in machine leaning techniques and ambiguous dataset which develops into a challenging issue, as the software product expands in terms of size and its complexity. This chapter reveals an applied novel multi-learner model which is ensembled to predict software metrics using classification algorithms and propose algorithm applied in parallel method for detection on ambiguous data using density sampling and develop an implementation running on both GPUs and multi-core CPUs. The defect on the NASA PROMISE defect dataset is adequately predicted and classified using these models and implementing GPU computing. The performance compared to the traditional learning models improved algorithm and parallel implementation on GPUs shows less processing time in ensemble model compared to decision tree algorithm and effectively optimizes the true positive rate.
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Conference papers on the topic "Software defect density"

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Shah, Syed Muhammad Ali, Maurizio Morisio, and Marco Torchiano. "Software defect density variants: A proposal." In 2013 4th International Workshop on Emerging Trends in Software Metrics (WETSoM). IEEE, 2013. http://dx.doi.org/10.1109/wetsom.2013.6619337.

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Shah, Syed Muhammad Ali, Maurizio Morisio, and Marco Torchiano. "An Overview of Software Defect Density: A Scoping Study." In 2012 19th Asia-Pacific Software Engineering Conference (APSEC). IEEE, 2012. http://dx.doi.org/10.1109/apsec.2012.93.

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Li, Zengyang, Peng Liang, and Bing Li. "Relating Alternate Modifications to Defect Density in Software Development." In 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C). IEEE, 2017. http://dx.doi.org/10.1109/icse-c.2017.132.

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Rahmani, Cobra, and Deepak Khazanchi. "A Study on Defect Density of Open Source Software." In 2010 IEEE/ACIS 9th International Conference on Computer and Information Science (ICIS). IEEE, 2010. http://dx.doi.org/10.1109/icis.2010.11.

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Rathaur, Suraj, Narayan Kamath, and Umesh Ghanekar. "Software Defect Density Prediction based on Multiple Linear Regression." In 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2020. http://dx.doi.org/10.1109/icirca48905.2020.9183110.

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Kutlubay, Onur, Burak Turhan, and Ayse B. Bener. "A Two-Step Model for Defect Density Estimation." In 33rd EUROMICRO Conference on Software Engineering and Advanced Applications (EUROMICRO 2007). IEEE, 2007. http://dx.doi.org/10.1109/euromicro.2007.13.

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Sherriff, Mark. "Utilizing verification and validation certificates to estimate software defect density." In the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium. ACM Press, 2005. http://dx.doi.org/10.1145/1081706.1081768.

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Gupta, Anita, Odd Petter N. Slyngstad, Reidar Conradi, Parastoo Mohagheghi, Harald Ronneberg, and Einar Landre. "A Case Study of Defect-Density and Change-Density and their Progress over Time." In 11th European Conference on Software Maintenance and Reengineering (CSMR'07). IEEE, 2007. http://dx.doi.org/10.1109/csmr.2007.5.

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Pradhan, Satya, Venky Nanniyur, and Pavan K. Vissapragada. "On the Defect Prediction for Large Scale Software Systems – From Defect Density to Machine Learning." In 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS). IEEE, 2020. http://dx.doi.org/10.1109/qrs51102.2020.00056.

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Mandhan, Neeraj, Dinesh Kumar Verma, and Shishir Kumar. "Analysis of approach for predicting software defect density using static metrics." In 2015 International Conference on Computing, Communication & Automation (ICCCA). IEEE, 2015. http://dx.doi.org/10.1109/ccaa.2015.7148499.

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