Using Fuzzy Logic in Test Case Prioritization for Regression Testing Programs with Assertions

Program assertions have been recognized as a supporting tool during software development, testing, and maintenance. Therefore, software developers place assertions within their code in positions that are considered to be error prone or that have the potential to lead to a software crash or failure. Similar to any other software, programs with assertions must be maintained. Depending on the type of modification applied to the modified program, assertions also might have to undergo some modifications. New assertions may also be introduced in the new version of the program, while some assertions can be kept the same. This paper presents a novel approach for test case prioritization during regression testing of programs that have assertions using fuzzy logic. The main objective of this approach is to prioritize the test cases according to their estimated potential in violating a given program assertion. To develop the proposed approach, we utilize fuzzy logic techniques to estimate the effectiveness of a given test case in violating an assertion based on the history of the test cases in previous testing operations. We have conducted a case study in which the proposed approach is applied to various programs, and the results are promising compared to untreated and randomly ordered test cases

Using Fuzzy Logic Techniques for Assertion-Based Software Testing Metrics

Software testing is a very labor intensive and costly task. Therefore, many software testing techniques to automate the process of software testing have been reported in the literature. Assertion-Based automated software testing has been shown to be effective in detecting program faults as compared to traditional black-box and white-box software testing methods. However, the applicability of this approach in the presence of large numbers of assertions may be very costly. Therefore, software developers need assistance while making decision to apply Assertion-Based testing in order for them to get the benefits of this approach at an acceptable level of costs. In this paper, we present an Assertion-Based testing metrics technique that is based on fuzzy logic. The main goal of the proposed technique is to enhance the performance of Assertion-Based software testing in the presence of large numbers of assertions. To evaluate the proposed technique, an experimental study was performed in which the proposed technique is applied on programs with assertions. The result of this experiment shows that the effectiveness and performance of Assertion-Based software testing have improved when applying the proposed testing metrics technique

Using Fuzzy Logic Techniques for Assertion-Based Software Testing Metrics

Software testing is a very labor intensive and costly task. Therefore, many software testing techniques to automate the process of software testing have been reported in the literature. Assertion-Based automated software testing has been shown to be effective in detecting program faults as compared to traditional black-box and white-box software testing methods. However, the applicability of this approach in the presence of large numbers of assertions may be very costly. Therefore, software developers need assistance while making decision to apply Assertion-Based testing in order for them to get the benefits of this approach at an acceptable level of costs. In this paper, we present an Assertion-Based testing metrics technique that is based on fuzzy logic. The main goal of the proposed technique is to enhance the performance of Assertion-Based software testing in the presence of large numbers of assertions. To evaluate the proposed technique, an experimental study was performed in which the proposed technique is applied on programs with assertions. The result of this experiment shows that the effectiveness and performance of Assertion-Based software testing have improved when applying the proposed testing metrics technique

Measuring the health literacy level of Arabic speaking population in Saudi Arabia using translated health literacy instruments

Background: Health literacy is an essential predictor of health status, disease control and adherence to medications. Objectives: The study goals were to assess the health literacy level of the general population in Saudi Arabia using translated Gulf Arabic version of the short-version of the Test of Functional Health Literacy in Adults (S-TOFHLA) and Single Item Literacy Screener (SILS) tests and to measure the relationship between health literacy and education level. Methods: The study was a cross-sectional with a convenience sample of 123 participants from the general population in Riyadh. Data were collected using the modified (Gulf) Arabic versions of both S-TOFHLA and SILS. Fisher’s Exact test was used to measure the difference of the health literacy scores according to the education degrees and Cronbach’s alpha was used to measure the internal consistency of the S-TOFHLA items. Results: More than half (55.4%) of the participants were male, 50.4% had a middle school or less education level, and we found that 84.4% had adequate health literacy as measured by the S-TOFHLA, compared to 49.6% as measured by SILS. The Fisher’s Exact test showed a significant difference (P<.05) in the S-TOFHLA and SILS scores according to education categories. Conclusions: The level of education has a significant positive association with S-TOFHLA and SILS results. The Gulf Arabic version of S-TOFHLA is a reliable test with a good internal consistency and a significant positive correlation between the two parts of S-TOFHLA. We recommend the use of S-TOFHLA or SILS at the first patient visit.

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population