Relevant bibliographies by topics / SOFTWARE DEFECT REPORTS

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Academic literature on the topic 'SOFTWARE DEFECT REPORTS'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "SOFTWARE DEFECT REPORTS"

1

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

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Changes in the software are unavoidable due to an ever changing dynamic and active environment wherein expectations and requirements of the users tend to change rapidly. As a result, software needs to upgrade itself from its previous version to the next version in order to meet expectations of the user. The upgradation of the software is in terms of total number of Lines of Code (LOC) that might have been inserted, deleted or modified in moving from one version of software to the next. These changes are maintained in the change reports which constitute of the defect ID and defect description. Defect description describes the cause of defect which might have occurred in the previous version of the software due to which either new LOC needs to be inserted or existing LOC need to be deleted or modified. A lot of effort is required to correct the defects identified in software at the maintenance phase i.e., when software is delivered at the customers end. Thus, in this paper, we intend to predict maintenance effort by analyzing the defect reports using text mining techniques and thereafter developing the prediction models using suitable machine learning algorithms viz. Multi-Layer Perceptron (MLP), Radial-Basis Function (RBF) network and Decision Tree (DT). We have considered the changes between three successive versions of ‘MMS’ application package of Android operating system and have performed inter-version validation where the model predicted using the version ‘v’ is validated on the subsequent version i.e., ‘v+1’. The performance of the model was evaluated using Receiver Operating Characteristics (ROC) analysis. The results indicated that the model predicted on ‘MMS’ 4.0 version using MLP algorithm has shown good results when validated on ‘MMS’ 4.1 version. On the other hand, the performance of RBF and DT algorithms has been consistently average in predicting the maintenance effort.
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2

Malhotra, Ruchika, Nidhi Kapoor, Rishabh Jain, and Sahaj Biyani. "Severity Assessment of Software Defect Reports using Text Classification." International Journal of Computer Applications 83, no. 11 (2013): 13–16. http://dx.doi.org/10.5120/14492-2622.

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3

Jindal, Rajni, Ruchika Malhotra, and Abha Jain. "Prediction of defect severity by mining software project reports." International Journal of System Assurance Engineering and Management 8, no. 2 (2016): 334–51. http://dx.doi.org/10.1007/s13198-016-0438-y.

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4

Marappan, Shanmugasundaram, Archana Kollu, Ismail Keshta, Shehab Mohamed Beram, Sahil Bhende, and Karthikeyan Kaliyaperumal. "An Optimized Systematic Approach to Identify Bugs in Cloud-Based Software." Scientific Programming 2022 (September 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/2302027.

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The resolution of a software bug depends on the severity of the defect report. Open-source software defect tracking solutions have taken over as the principal means of processing enormous amounts of defect information data due to the ongoing increase in software scale. Dealing with software faults requires analyzing the implications of defect report severity in the data warehouse. Thus, the authors have proposed an optimized systematic approach through the research and analysis of Bugzilla defect tracking system data in this study, where it is found that the attribute characteristics of different projects are quite different and the statistical features of the repair rate, resolution time, developers, components, and other attributes are consistent. This technique, therefore, assumes that a rise in the severity of software defect reports will result in a rise in the defect repair rate and that the severity is normally based on the severity distribution of various components and projects. According to the study’s findings, developers hold the most defects when the repair rate is low and the defect resolution time is shortest.
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5

Mellegard, Niklas, Hakan Burden, Daniel Levin, Kenneth Lind, and Ana Magazinius. "Contrasting Big Bang With Continuous Integration Through Defect Reports." IEEE Software 37, no. 3 (2020): 14–20. http://dx.doi.org/10.1109/ms.2018.2880822.

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6

Sultan, Torky, Ayman E. Khedr, and Mostafa Sayed. "A Proposed Defect Tracking Model for Classifying the Inserted Defect Reports to Enhance Software Quality Control." International Journal of Computer Applications 67, no. 14 (2013): 1–7. http://dx.doi.org/10.5120/11460-7068.

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Sultan, Torky, Ayman Khedr, and Mostafa Sayed. "A Proposed Defect Tracking Model for Classifying the Inserted Defect Reports to Enhance Software Quality Control." Acta Informatica Medica 21, no. 2 (2013): 103. http://dx.doi.org/10.5455/aim.2013.21.103-108.

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8

Yadla, Suresh, Jane Huffman Hayes, and Alex Dekhtyar. "Tracing requirements to defect reports: an application of information retrieval techniques." Innovations in Systems and Software Engineering 1, no. 2 (2005): 116–24. http://dx.doi.org/10.1007/s11334-005-0011-3.

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9

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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10

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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