Feedback driven adaptive combinatorial testing

The configuration spaces of modern software systems are too large to test exhaustively. Combinatorial interaction testing (CIT) approaches, such as covering arrays, systematically sample the configuration space and test only the selected configurations. The basic justification for CIT approaches is that they can cost-effectively exercise all system behaviors caused by the settings of t or fewer options. We conjecture, however, that in practice many such behaviors are not actually tested because of masking effects – failures that perturb execution so as to prevent some behaviors from being exercised. In this work we present a feedback-driven, adaptive, combinatorial testing approach aimed at detecting and working around masking effects. At each iteration we detect potential masking effects, isolate their likely causes, and then generate new covering arrays that allow previously masked combinations to be tested in the subsequent iteration. We empirically assess the effectiveness of the proposed approach on two large widely-used open source software systems. Our results suggest that masking effects do exist and that our approach provides a promising and effcient way to work around them

Feedback driven adaptive combinatorial testing

The configuration spaces of modern software systems are too large to test exhaustively. Combinatorial interaction testing (CIT) approaches, such as covering arrays, systematically sample the configuration space and test only the selected configurations. The basic justification for CIT approaches is that they can cost-effectively exercise all system behaviors caused by the settings of t or fewer options. We conjecture, however, that in practice many such behaviors are not actually tested because of masking effects – failures that perturb execution so as to prevent some behaviors from being exercised. In this work we present a feedback-driven, adaptive, combinatorial testing approach aimed at detecting and working around masking effects. At each iteration we detect potential masking effects, heuristically isolate their likely causes, and then generate new covering arrays that allow previously masked combinations to be tested in the subsequent iteration. We empirically assess the effectiveness of the proposed approach on two large widely used open source software systems. Our results suggest that masking effects do exist and that our approach provides a promising and efficient way to work around them

Geribesleme güdümlü adaptif kombinasyonel test etme yaklaşımı

The configuration space of a software system forms a combinatorial space, whose large size generally makes exhaustive testing infeasible. Combinatorial interaction testing (CIT) approaches systematically sample the configuration space and test only the selected configurations. The basic justification for CIT approaches, such as covering arrays, is that they can cost-effectively exercise all system behaviors caused by the settings of t or fewer options. We conjecture however that in practice many such behaviors are not actually tested in the presence of what we call masking effects failures perturbing behavior in ways that prevent some intended behaviors from being tested. In this work we present a feedback driven adaptive combinatorial testing approach aimed at detecting and working around masking effects. At each iteration of this approach, we detect potential masking effects, heuristically isolate their likely causes, and then generate new covering arrays that allow previously masked combinations to be tested in the subsequent iteration. We, furthermore, empirically assess the effectiveness of the proposed approach by using a large widely used open source software system as our subject application

Investigation of the protective effect of gel incorporating Eugenia jambolana leaf extract on 5‑fluorouracil‑induced oral mucositis: an animal study

Purpose The study aimed to evaluate the possible preventive effect of two concentrations (3 and 5% w/w) of Eugenia jambolana (EJ) extract against 5-FU-induced mucositis. Method Sixteen adult rats were separated into four groups: two control and two preventive groups. Animals in Groups 1, 2, and 3 were injected intraperitoneally with 60 mg/kg/day of 5-FU on Day 1 followed by 150 mg/kg/day on Day 5. The rats in Group 4 (negative control) were given physiological saline at the same times and doses. Furthermore, on the fifth day of the study, the cheek and sublingual mucosa were irritated by external superficial scratches using the tip of an 18-G needle, followed by the application 15 μL of 20% acetic acid, after which 3 and 5% EJ w/w gels were applied topically for animals in Groups 2 and 3, respectively. Results The weight and the mucositis scores were recorded. Antioxidant and anti-inflammatory markers and biochemical tests were analyzed. Significant differences were found between the study groups in weight loss, clinical mucositis scores, mortality rates, and antioxidant and anti-inflammatory parameters. Conclusion The preventive effect of 3% gel was significant, with no mortality rate, making it an option for preventive strategies

API Message-Driven Regression Testing Framework

With the increase in the number of APIs and interconnected applications, API testing has become a critical part of the software testing process. Particularly considering the business-critical systems using API messages, the importance of repetitive API tests increases. Successfully performing repetitive manual API testing for a large number of test scenarios in large business enterprise applications becomes even more difficult due to the fact that human errors may prevent performing thousands of human-written tests with high precision every time. Furthermore, the existing API test automation tools used in the market cannot be integrated into all business domains due to their dependence on applications. These tools generally support web APIs over the HTTP protocol. Hence, this study is motivated by the fact that there is a lack of API message-driven regression testing frameworks in a particular area in which API messages are used in client-server communication. This study has been prepared to close the gap in a specific domain which uses business domain APIs, rather than HTTP, in client-server communication. We propose a novel approach based on the use of network packets for regression testing. We developed a proof-of-concept test automation tool implementing our approach and evaluated it in a financial domain. Unlike prior studies, our approach can provide the use of real data packets in software testing. The use of network packets increases the generalization of the framework. Overall, our study reports remarkable reuse capacity and makes a significant impact on a real-world business-critical system by reducing effort and increasing the automation level of API regression testing

API Message-Driven Regression Testing Framework

With the increase in the number of APIs and interconnected applications, API testing has become a critical part of the software testing process. Particularly considering the business-critical systems using API messages, the importance of repetitive API tests increases. Successfully performing repetitive manual API testing for a large number of test scenarios in large business enterprise applications becomes even more difficult due to the fact that human errors may prevent performing thousands of human-written tests with high precision every time. Furthermore, the existing API test automation tools used in the market cannot be integrated into all business domains due to their dependence on applications. These tools generally support web APIs over the HTTP protocol. Hence, this study is motivated by the fact that there is a lack of API message-driven regression testing frameworks in a particular area in which API messages are used in client-server communication. This study has been prepared to close the gap in a specific domain which uses business domain APIs, rather than HTTP, in client-server communication. We propose a novel approach based on the use of network packets for regression testing. We developed a proof-of-concept test automation tool implementing our approach and evaluated it in a financial domain. Unlike prior studies, our approach can provide the use of real data packets in software testing. The use of network packets increases the generalization of the framework. Overall, our study reports remarkable reuse capacity and makes a significant impact on a real-world business-critical system by reducing effort and increasing the automation level of API regression testing

Program yürütmelerini sınıflandırmak için donanım ve yazılım ölçüm aygıtlarını birleştirme (Combining hardware and software instrumentation to classify program executions)

Pek çok çalışma, genellikle yazılım odaklı ölçüm aygıtları yardımı ile, program yürütmelerinden elde edilen program tayflarını, yazılım sistemlerinin davranışsal özelliklerini ortaya çıkarmak için kullanmıştır. Bu genel yaklaşımın önemli uygulamalarından biri ise, başarısız yürütmelerin, başarılı yürütmelerden otamatik olarak ayırt edilmesidir. Bahsi geçen amaca yönelik literatürde yer alan bir çok çalışmanın, doğru sınıflandırmalar ürettiği görülürken, kullanılan yöntemlerin ek yük masrafı ve sınıflandırma başarası arasındaki ödünleşimin dengelenmesi hususu üzerinde sistematik çalışmaların sayısı azdır. Bu çalışma, donanım performans sayaçlarını kullanarak düşük maliyetlerde yürütmelerden veri toplanabilmesine olanak sağlayan çeşitli hibrid program tayfları önermektedir. Önerilen yaklaşım, düşük ek yük masraflı donanım tayflarını, az sayıda ama daha yüksek masraflı yazılım ölçüm aygıtlarıyla birleştirmektedir. Bahsi geçen yaklaşım, diğer temsili yaklaşımlarla birlikte karşılaştırmalı olarak deneysel yöntemlerle değerlendirilmektedir. Deneysel çalışmaların sonuçları, yeni hibrid program tayflarının, güvenilir ve etkili (az masraflı) bir şekilde yürütmeleri sınıflandırabileceğini destekler niteliktedir

Reducing masking effects in combinatorial interaction testing: a feedback driven adaptive approach

The configuration spaces of modern software systems are too large to test exhaustively. Combinatorial interaction testing (CIT) approaches, such as covering arrays, systematically sample the configuration space and test only the selected configurations. The basic justification for CIT approaches is that they can cost-effectively exercise all system behaviors caused by the settings of t or fewer options. We conjecture, however, that in practice some of these behaviors are not actually tested because of unanticipated masking effects - test case failures that perturb system execution so as to prevent some behaviors from being exercised. While prior research has identified this problem, most solutions require knowing the masking effects a priori. In practice this is impractical, if not impossible. In this work, we reduce the harmful consequences of masking effects. First we define a novel interaction testing criterion, which aims to ensure that each test case has a fair chance to test all valid t-way combinations of option settings. We then introduce a feedback driven adaptive combinatorial testing process (FDA-CIT) to materialize this criterion in practice. At each iteration of FDA-CIT, we detect potential masking effects, heuristically isolate their likely causes (i.e., fault characterization), and then generate new samples that allow previously masked combinations to be tested in configurations that avoid the likely failure causes. The iterations end when the new interaction testing criterion has been satisfied. This paper compares two different fault characterization approaches - an integral part of the proposed approach, and empirically assesses their effectiveness and efficiency in removing masking effects on two widely used open source software systems. It also compares FDA-CIT against error locating arrays, a state of the art approach for detecting and locating failures. Furthermore, the scalability of the proposed approach is evaluated by comparing it with perfect test scenarios, in which all masking effects are known a priori. Our results suggest that masking effects do exist in practice, and that our approach provides a promising and efficient way to work around them, without requiring that masking effects be known a priori

Poor-quality sleep score is an independent predictor of nondipping hypertension

WOS: 000279875000003PubMed: 20639701Objective We aimed to investigate whether there was any association between the nondipping status and sleep quality in relatively young patients with an initial diagnosis of hypertension. Methods One hundred and thirty-three consecutive patients, diagnosed to have stage 1 hypertension by their primary physicians, were referred to our study. Patients with a history of use of any antihypertensive medication were excluded. Eligible patients underwent the Pittsburgh Sleep Quality Index (PSQI) survey, which has an established role in evaluating sleep disturbances. All patients underwent ambulatory blood pressure monitoring. Results There were 71 nondipper patients (mean age 44.3 +/- 5.3 years, 33 male/38 female) and 62 dipper hypertensive patients (mean age 43.3 +/- 6.3 years, 27 male/35 female). The PSQI scores, globally, were significantly higher in the nondippers compared with the dippers. It was noticed that all the components of the PSQI (sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbance, use of sleep medication, and daytime dysfunction) were significantly higher in the nondippers. Correlation analysis showed that systolic blood pressure fall at night was inversely and significantly related with the PSQI (r = -0.46, P < 0.001). Logistic regression analysis showed that the PSQI score is an independent determinant for nondipping hypertension (HT) {odds ratio = 0.842 [95% confidence interval (CI) = 0.748-0.947; P = 0.004]}. Conclusion We showed that poor sleep quality was related with a nondipping pattern, and furthermore, it was an independent predictor of nondipping in newly diagnosed stage 1 hypertensive patients Blood Press Monit 15:184-187 (C) 2010 Wolters Kluwer Health vertical bar Lippincott Williams & Wilkins