Test case selection and prioritization using machine learning: a systematic literature review

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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A Machine Learning Based Approach for Software Test Case Selection

Proceedings of Intelligent Computing and Technologies Conference ◽

10.21467/proceedings.115.25 ◽

2021 ◽

Author(s):

Victor Cheruiyot ◽

Baidya Nath Saha

Keyword(s):

Machine Learning ◽

Language Processing ◽

Software Test ◽

Selection Strategy ◽

Test Case ◽

Test Cases ◽

Domain Experts ◽

Test Case Selection ◽

Case Selection ◽

Text Feature

Testing is conducted after developing each software to detect the defects which are then removed. However, it is very difficult task to test a non-trivial software completely. Hence, it’s important to test the software with important test cases. In this research, we developed a machine learning based software test case selection strategy for regression testing. To develop the method, we first clean and preprocess the data. Then we convet the categorical data to its numerical value. The we implement a natural language processing to calculate bag of features for text feature such as testcase title. We evaluate different machine learning models for test case selection. Experimental results demonstrate that machine learning based models can aovid manual labour of the domain experts for test case selection.

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Federated Learning in a Medical Context: A Systematic Literature Review

ACM Transactions on Internet Technology ◽

10.1145/3412357 ◽

2021 ◽

Vol 21 (2) ◽

pp. 1-31

Author(s):

Bjarne Pfitzner ◽

Nico Steckhan ◽

Bert Arnrich

Keyword(s):

Machine Learning ◽

Literature Review ◽

Systematic Literature Review ◽

Data Privacy ◽

Research Area ◽

Learning Models ◽

Related Data ◽

Private Data ◽

Large Databases ◽

Machine Learning Models

Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.

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Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

Global Business Review ◽

10.1177/0972150920984857 ◽

2021 ◽

pp. 097215092098485

Author(s):

Sonika Gupta ◽

Sushil Kumar Mehta

Keyword(s):

Machine Learning ◽

Data Mining ◽

Literature Review ◽

Systematic Literature Review ◽

Classification Accuracy ◽

Meta Analysis ◽

Financial Statement ◽

Research Articles ◽

Financial Statement Fraud ◽

Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

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Machine learning approaches to IoT security: A systematic literature review

Internet of Things ◽

10.1016/j.iot.2021.100365 ◽

2021 ◽

Vol 14 ◽

pp. 100365

Author(s):

Rasheed Ahmad ◽

Izzat Alsmadi

Keyword(s):

Machine Learning ◽

Literature Review ◽

Systematic Literature Review ◽

Learning Approaches ◽

Iot Security

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Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01403-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Alan Brnabic ◽

Lisa M. Hess

Keyword(s):

Machine Learning ◽

Decision Making ◽

Literature Review ◽

Systematic Literature Review ◽

Real World ◽

Learning Algorithms ◽

External Validation ◽

Machine Learning Algorithms ◽

Learning Methods ◽

Machine Learning Methods

Abstract Background Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. Methods This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. Results A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. Conclusions A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

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