Software Testing Effort Estimation and Related Problems

Bluemke, Ilona; Malanowska, Agnieszka

doi:10.1145/3442694

Cited by 16 publications

(11 citation statements)

References 72 publications

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“…Some publications deal with test optimization issues: prioritization, reduction, and selection. Test prioritization is focused on test execution ordering to detect the highest number of faults at the earliest [18] - [21]. Test reduction and selection is correlated with checking their results and usage during project development [21].…”

Section: A Test Challengesmentioning

confidence: 99%

“…Test prioritization is focused on test execution ordering to detect the highest number of faults at the earliest [18] - [21]. Test reduction and selection is correlated with checking their results and usage during project development [21]. Program diagnosability [22] can also impact quality of developed tests.…”

Section: A Test Challengesmentioning

confidence: 99%

“…Nevertheless, in the second column of Table 1, we list indicative publications focused on research areas which, in our opinion, can be combined with testing. It is worth commenting the contents of the recent survey paper on software testing [21]. It covers over 170 positions (including some other surveys).…”

Section: B Test Assessmentmentioning

confidence: 99%

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Extending the Space of Software Test Monitoring: Practical Experience

Lasynskyi

Sosnowski

2021

IEEE Access

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Software reliability depends on the performed tests. Bug detection and diagnosis are based on test outcome (oracle) analysis. Most practical test reports do not provide sufficient information for localizing and correcting bugs. We have found the need to extend the space of test result observation in data and time perspectives. This resulted in tracing supplementary test result features in event logs. They are explored with combined text mining and log parsing techniques. Another important point is correlating test life cycle with project development history journaled in issue tracking and software version control repositories. Dealing with the outlined problems, neglected in the literature, we have introduced original analysis schemes. They focus on assessing test coverage, reasons of low diagnosability, and test result profiles. Multidimensional investigation of test features and their management is supported with the developed test infrastructure. This assures a holistic insight into the test efficiency to identify test scheme deficiencies (e.g., functional inadequacy, aging, insufficient coverage) and possible improvements (test set updates). Our studies have been verified in relevance to a real commercial project (industrial case study) and confronted with the experience of testers engaged in other projects.INDEX TERMS bug diagnostics, software defect repository, software testing, testing framework, test monitoring.

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Section: A Test Challengesmentioning

confidence: 99%

Section: A Test Challengesmentioning

confidence: 99%

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Extending the Space of Software Test Monitoring: Practical Experience

Lasynskyi

Sosnowski

2021

IEEE Access

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“…Most of this research has been focused on the prediction of the total effort for the project. Limited literature focuses on predicting effort for particular phases, like testing, even though it is a significant and challenging area [6,7]. Thus, the first motivation for this study is related to the importance of the outcome variable for prediction, i.e., testing effort.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Data Quality on Software Testing Effort Prediction

Radliński

2023

Electronics

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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, based on the Wilcoxon signed-rank test, was applied to identify the best models and data variants. Results: Most models, especially the most accurate, performed the best on a complete dataset, even though it contained cases with low data ratings. The detailed results include the rankings of the following: (1) models for particular data variants, (2) data variants for particular models, and (3) the best-performing combinations of models and data variants. Conclusions: Arbitrary and restrictive data selection to only projects with Data Quality Rating and UFP Rating of ‘A’ or ‘B’, commonly used in the literature, does not seem justified. It is recommended not to exclude cases with low data ratings to achieve better accuracy of most predictive models for testing effort prediction.

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“…The elementary methodology of conducting an SLR is always similar, regardless of the discipline being reviewed. There are many recent SLRs in software testing and reliability [9][10][11][12][13]. Reviews of secondary studies have also been conducted in the area of software testing [14].…”

Section: Introductionmentioning

confidence: 99%

Systematic Literature Review on Test Case Selection and Prioritization: A Tertiary Study

Singhal

Jatana²,

Suri

et al. 2021

Applied Sciences

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Software testing is undertaken to ensure that the software meets the expected requirements. The intention is to find bugs, errors, or defects in the developed software so that they can be fixed before deployment. Testing of the software is needed even after it is deployed. Regression testing is an inevitable part of software development, and must be accomplished in the maintenance phase of software development to ensure software reliability. The existing literature presents a large amount of relevant knowledge about the types of techniques and approaches used in regression test case selection and prioritization (TCS&P), comparisons of techniques used in TCS&P, and the data used. Numerous secondary studies (surveys or reviews) have been conducted in the area of TCS&P. This study aimed to provide a comprehensive examination of the analysis of the enhancements in TCS&P using a thorough systematic literature review (SLR) of the existing secondary studies. This SLR provides: (1) a collection of all the valuable secondary studies (and their qualitative analysis); (2) a thorough analysis of the publications and the trends of the secondary studies; (3) a classification of the various approaches used in the secondary studies; (4) insight into the specializations and range of years covered in the secondary texts; (5) a comprehensive list of statistical tests and tools used in the area; (6) insight into the quality of the secondary studies based on the seven selected Research Paper Quality parameters; (7) the common problems and challenges encountered by researchers; (8) common gaps and limitations of the studies; and (9) the probable prospects for research in the field of TCS&P.

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Software Testing Effort Estimation and Related Problems

Cited by 16 publications

References 72 publications

Extending the Space of Software Test Monitoring: Practical Experience

Extending the Space of Software Test Monitoring: Practical Experience

The Impact of Data Quality on Software Testing Effort Prediction

Systematic Literature Review on Test Case Selection and Prioritization: A Tertiary Study

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