Investigating software testing and maintenance reports: Case study

Janczarek, Paweł; Sosnowski, J.

doi:10.1016/j.infsof.2014.06.015

Cited by 13 publications

(4 citation statements)

References 33 publications

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“…Practical significance of LIMCR in software engineering might be extended. Project members can obtain information on possible defect-prone modules of the software before failure occur by using defect prediction technique, LIMCR has not been applied to predict defect classes/severities [64].…”

Section: Threats To Validitymentioning

confidence: 99%

LIMCR: Less-Informative Majorities Cleaning Rule Based on Naïve Bayes for Imbalance Learning in Software Defect Prediction

Yao

Chang

et al. 2020

Applied Sciences

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Software defect prediction (SDP) is an effective technique to lower software module testing costs. However, the imbalanced distribution almost exists in all SDP datasets and restricts the accuracy of defect prediction. In order to balance the data distribution reasonably, we propose a novel resampling method LIMCR on the basis of Naïve Bayes to optimize and improve the SDP performance. The main idea of LIMCR is to remove less-informative majorities for rebalancing the data distribution after evaluating the degree of being informative for every sample from the majority class. We employ 29 SDP datasets from the PROMISE and NASA dataset and divide them into two parts, the small sample size (the amount of data is smaller than 1100) and the large sample size (larger than 1100). Then we conduct experiments by comparing the matching of classifiers and imbalance learning methods on small datasets and large datasets, respectively. The results show the effectiveness of LIMCR, and LIMCR+GNB performs better than other methods on small datasets while not brilliant on large datasets.

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Section: Threats To Validitymentioning

confidence: 99%

LIMCR: Less-Informative Majorities Cleaning Rule Based on Naïve Bayes for Imbalance Learning in Software Defect Prediction

Yao

Chang

et al. 2020

Applied Sciences

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“…This smart lighting system with open API integrates seamlessly with the IT system and enables a variety of software applications to create a more intelligent work environment for both building operations managers and employees. In literature, the use of software development schemes and repositories are well described [32][33][34]. It is demonstrated that detailed problem handling reports will extend the space and quality of statistical analysis for software [34].…”

Section: Case 2: Industrial Datamentioning

confidence: 99%

“…It is demonstrated that detailed problem handling reports will extend the space and quality of statistical analysis for software [34]. It is without doubts that detailed reports will provide significant improvement of software development processes as well as better reliability predictions [32]. In this case, the bug reports may come from different sources (implemented regression tests and tests by the team).…”

Section: Case 2: Industrial Datamentioning

confidence: 99%

Software Reliability for Agile Testing

Driel

Bikker²,

Tijink³

et al. 2020

Mathematics

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It is known that quantitative measures for the reliability of software systems can be derived from software reliability models, and, as such, support the product development process. Over the past four decades, research activities in this area have been performed. As a result, many software reliability models have been proposed. It was shown that, once these models reach a certain level of convergence, it can enable the developer to release the software and stop software testing accordingly. Criteria to determine the optimal testing time include the number of remaining errors, failure rate, reliability requirements, or total system cost. In this paper, we present our results in predicting the reliability of software for agile testing environments. We seek to model this way of working by extending the Jelinski–Moranda model to a “stack” of feature-specific models, assuming that the bugs are labeled with the features they belong to. In order to demonstrate the extended model, two use cases are presented. The questions to be answered in these two cases are: how many software bugs remain in the software and should one decide to stop testing the software?

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“…Many software reliability assessment methods based on the stochastic model have been proposed by several researchers [6][7][8]. Recently, it is difficult to assess the software reliability because there are various software development styles.…”

Section: Sectionmentioning

confidence: 99%

Large Scale Fault Data Analysis and OSS Reliability Assessment Based on Quantification Method of the First Type

Tamura

Yamada

2020

MAKE

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Various big data sets are recorded on the server side of computer system. The big data are well defined as a volume, variety, and velocity (3V) model. The 3V model has been proposed by Gartner, Inc. as a first press release. 3V model means the volume, variety, and velocity in terms of data. The big data have 3V in well balance. Then, there are various categories in terms of the big data, e.g., sensor data, log data, customer data, financial data, weather data, picture data, movie data, and so on. In particular, the fault big data are well-known as the characteristic log data in software engineering. In this paper, we analyze the fault big data considering the unique features that arise from big data under the operation of open source software. In addition, we analyze actual data to show numerical examples of reliability assessment based on the results of multiple regression analysis well-known as the quantification method of the first type.

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Investigating software testing and maintenance reports: Case study

Cited by 13 publications

References 33 publications

LIMCR: Less-Informative Majorities Cleaning Rule Based on Naïve Bayes for Imbalance Learning in Software Defect Prediction

LIMCR: Less-Informative Majorities Cleaning Rule Based on Naïve Bayes for Imbalance Learning in Software Defect Prediction

Software Reliability for Agile Testing

Large Scale Fault Data Analysis and OSS Reliability Assessment Based on Quantification Method of the First Type

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