Academic literature on the topic 'OPTIMIZED TEST CASES'

Detection of fault proneness classes helps software testers to generate effective class level test cases. In this article, a novel technique is presented for an optimized test case generation for ant-1.7 open source software. Class level object oriented (OO) metrics are considered as effective means to find fault proneness classes. The open source software ant-1.7 is considered for the evaluation of proposed techniques as a case study. The proposed mathematical model is the first of its kind generated using Weka open source software to select effective OO metrics. Effective and ineffective OO metrics are identified using feature selection techniques for generating test cases to cover fault proneness classes. In this methodology, only effective metrics are considered for assigning weights to test paths. The results indicate that the proposed methodology is effective and efficient as the average fault exposition potential of generated test cases is 90.16% and test cases execution time saving is 45.11%.

Abstract The serum pancreolauryl test has limited sensitivity for detecting mild pancreatic insufficiency. The aim of this study was to optimize the serum pancreolauryl test so as to increase the probability of positive results in patients with chronic pancreatitis. The study had three parts. First, the sampling time was optimized by analyzing retrospectively the frequency of fluorescein peaks at different times from 0 to 240 min in 560 consecutive patients. Second, the calculation of serum fluorescein concentrations by means of a standard calibration factor was prospectively compared in 271 consecutive patients with a modification involving a specimen-specific calibration factor for each patient. Third, the clinical utility of the intravenous injection of secretin before ingestion of the test meal was prospectively evaluated in a further 32 patients. As a result, the optimized serum pancreolauryl test developed differs from the former version of the test in utilizing intravenous administration of secretin before the test meal, calculation of serum fluorescein based on specimen-specific calibration factors, and blood samples taken only at 0 (basal), 120, 150, 180, and 240 min. This optimized pancreolauryl test was abnormal more frequently in patients with chronic pancreatitis than was the formerly used test, especially for cases of mild and moderate disease.

Software once developed is subject to continuous changes. Software Regression Testing thus can be used to reduce the efforts of testing the software by selecting only the required number of test cases and ordering them to test the software after changes have been made to it. In order to improve the fault detection rate, the selection of efficient test cases and order of execution of these tests is important. Here is when the test case selection comes into picture where in, the fault detection rate during the working of any software has to be improved. The test case selection process will find the most efficient test cases which can fully functionally test the software that has been modified. This indeed will contribute to an improved fault detection rate which can provide faster feedback on the system under test and let software engineers begin correcting faults as early as possible. In this paper, an approach for test case selection is proposed which takes into consideration the effect of three parameters History, Coverage and Requirement all together in order to improve the selection process. This will also ensure that the rejection of some efficient test cases is reduced when compared to the selection process in conventional methods, most of them making use of a single parameter for test case selection. These Test cases are further optimized using Genetic Algorithm. Results indicate that the proposed technique is much more efficient in terms of selecting the test cases when compared to conventional techniques, thereby improving fault detection rate.

Purpose. To: 1) optimize algorithm of stress echocardiography (s-Echo) with intravenous adenosine triphosphate (ATP) infusion taking into account pharmacokinetics and pharmacodynamics of ATP in human body, 2) test new algorithm in patients with coronary and other heart diseases. Materials and methods. In order to determine spectrum of factors influencing the results of stress test with ATP we inspected main scientific data bases and found 48 publications on ATP application for diagnostic purposes. Analysis of these publications allowed us to optimize algorithm of ATP s-Echo. Optimized algorithm was tested on 26 subjects, who underwent ATP 4D strain-stress-echocardiography of the left ventricle. Results and discussion. Optimized algorithm has three stages: registration of Echo data sets before, at the time of ATP infusion, and after 5 min of ATP infusion termination. Registration of Echo parameters at the second stage must begin not earlier than 3 min after the onset of ATP infusion and only in the presence of signs of coronary vasodilation. We think that the main indirect criterion of submaximal coronary vasodilation is 5 mm Hg or more decrease in systolic blood pressure (SBP), but not below SBP level of 90 mm Hg. Initial dose of ATP is 140 µg/kg/min. If after 2 min of infusion SBP do not diminish we increase the infusion rate at first to 175 and then to 210 µg/kg/min. While testing new algorithm in all cases we have achieved criteria of effective vasodilation. Mean SBP decrease was 16.4±13.7 mm Hg, heart rate increase – 12.7±8.1 bpm. In all patients we obtained interpretable 4D LV Echo data sets for visual analysis of local contractility and automatic strain analysis. Conclusion. Optimization of ATP s­Echo algorithm was performed. Safety and efficacy of optimized algorythm for registration of echo data was demonstrated. New ATP infusion algorithm can also be recommended for testing with other cardiac imaging modalities in evaluation of myocardial perfusion and contractility (SPECT, CT, MRI, PET).

The aim: To optimize the treatment of children with Essential Arterial Hypertension (EAH) in assotiation with Endotelial Dysfunction (ED) by studying the clinical and morphofunctional characteristics of the cardiovascular system disorders and correction of endothelial dysfunction with the using of essential phospholipids. Materials and methods: The study group consisted of 80 children and 30 – a control group. The next stage included the division of 80 children into 2 subgroups. Patients in the first subgroup received basic treatment (angiotensin-converting enzyme inhibitor of the third generation), the second – optimized treatment (basic treatment was with addition of certified drug lecithin). Doses were determined according to the instructions and age for 2 months. In the study were used: ECG, Echocardiography, Ultrasonography, Morphofunctional studies of the endothelium. Results: There is a dynamic decreasing in the level of left ventricular myocardial mass index (LV MMI), reduction of end-diastolic volume (EDV) and increase in the absolute values of shock volume (SV), ejection fraction( EF) under the influence of optimized treatment due to the inclusion of lecithin in the treatment of children with EAH with ED. The Ve/Va ratio had a tendency to increase. Vasoconstriction of vessels after the reactive hyperemia test was significantly reduced, but the degree of vasodilation varied depending on the method of therapy. The intima-media thickness (IMT) decreased in 1.12 times in the cases of children with an optimized treatment, accompanied by a decreasing of DEC by 2-times. Levels of the aortic stiffness index had a tendency of decreasing (from 0.88 ± 0.02 to 0.71 ± 0.01 and to 0.63 ± 0.01, respectively, by groups and in comparison with the control group – 0.55 ± 0 , 01), which reflects the improvement of hemodynamic parameters. The dynamic parameters obtained in the cases of patients with EAH in association with ED, taking into account the impact of the optimized treatment had positive correction on the total risk of cardiovascular complications, changes in the profile of LV diastolic filling, dysfunction of arterial endothelium. Conclusions: The inclusion of essential phospholipids in the treatment of children with EAH and ED helps to optimize the profile of LV diastolic filling and exclude vascular endothelial dysfunction and indicate a positive effect of optimized treatment on the overall risk of cardiovascular complications.

The software is growing in size and complexity every day due to which strong need is felt by the research community to search for the techniques which can optimize test cases effectively. The current study is inspired by the collective behavior of finding paths from the colony of food and uses different versions of Hill Climbing Algorithm (HCA) such as Stochastic, and Steepest Ascent HCA for the purpose of finding a good optimal solution. The performance of the proposed algorithm is verified on the basis of three parameters comprising of optimized test cases, time is taken during the optimization process, and the percentage of optimization achieved. The results suggest that proposed Stochastic HCA is significantly average percentage better than Steepest Ascent HCA in reducing the number of test cases in order to accomplish the optimization target.

In this paper, we address the management of Data Centers (DCs) by considering their optimal integration with the electrical, thermal, and IT (Information Technology) networks helping them to meet sustainability objectives and gain primary energy savings. Innovative scenarios are defined for exploiting the DCs electrical, thermal, and workload flexibility as a commodity and Information and Communication Technologies (ICT) are proposed and used as enablers for the scenarios’ implementation. The technology and scenarios were evaluated in the context of two operational DCs: a micro DC in Poznan which has on-site renewable sources and a DC in Point Saint Martin. The test cases’ results validate the possibility of using renewable energy sources (RES) for exploiting DCs’ energy flexibility and the potential of combining IT load migration with the availability of RES to increase the amount of energy flexibility by finding a trade-off between the flexibility level, IT load Quality of Service (QoS), and the RES production level. Moreover, the experiments conducted show that the DCs can successfully adapt their thermal energy profile for heat re-use as well as the combined electrical and thermal energy profiles to match specific flexibility requests.

The primary objective of software testing is to locate bugs as many as possible in software by using an optimum set of test cases. Optimum set of test cases are obtained by selection procedure which can be viewed as an optimization problem. So metaheuristic optimizing (searching) techniques have been immensely used to automate software testing task. The application of metaheuristic searching techniques in software testing is termed as Search Based Testing. Non-redundant, reliable and optimized test cases can be generated by the search based testing with less effort and time. This article presents a systematic review on several meta heuristic techniques like Genetic Algorithms, Particle Swarm optimization, Ant Colony Optimization, Bee Colony optimization, Cuckoo Searches, Tabu Searches and some modified version of these algorithms used for test case generation. The authors also provide one framework, showing the advantages, limitations and future scope or gap of these research works which will help in further research on these works.

Test case selection helps in improving quality of test suites by removing ambiguous, redundant test cases, thereby reducing the cost of software testing. Various works carried out have chosen test cases based on single parameter and optimized the test cases using single objective employing single strategies. In this article, a parameter selection technique is combined with an optimization technique for optimizing the selection of test cases. A two-step approach has been employed. In first step, the fuzzy entropy-based filtration is used for test case fitness evaluation and selection. In second step, the improvised ant colony optimization is employed to select test cases from the previously reduced test suite. The experimental evaluation using coverage parameters namely, average percentage statement coverage and average percentage decision coverage along with suite size reduction, demonstrate that by using this proposed approach, test suite size can be reduced, reducing further the computational effort incurred.

At the time the world is attempting to get over the damage caused by the COVID-19 spread, the monkeypox virus threatens to evolve into a global pandemic. Human monkeypox was first recognized in Africa and has recently emerged in 103 countries outside Africa. However, monkeypox diagnosis in an early stage is difficult because of the similarity between it, chickenpox, cowpox and measles. In some cases, computer-assisted detection of monkeypox lesions can be helpful for quick identification of suspected cases. Infected and uninfected cases have added to a growing dataset that is publicly accessible and may be utilized by machine and deep learning to predict the suspected cases at an early stage. Motivated by this, a diagnostic framework to categorize the cases of patients into four categories (i.e., normal, monkeypox, chicken pox and measles) is proposed. The diagnostic framework is a hybridization of pre-trained Convolution Neural Network (CNN) models, machine learning classifiers and a metaheuristic optimization algorithm. The hyperparameters of the five pre-trained models (i.e., VGG19, VGG16, Xception, MobileNet and MobileNetV2) are optimized using a Harris Hawks Optimizer (HHO) metaheuristic algorithm. After that, the features can be extracted from the feature extraction and reduction layers. These features are classified using seven machine learning models (i.e., Random Forest, AdaBoost, Histogram Gradient Boosting, Gradient Boosting, Support Vector Machine, Extra Trees and KNN). For each classifier, 10-fold cross-validation is used to train and test the classifiers on the features and the weighted average performance metrics are reported. The predictions from the pre-trained model and machine learning classifiers are then processed using majority voting. This study conducted the experiments on two datasets (i.e., Monkeypox Skin Images Dataset (MSID) and Monkeypox Images Dataset (MPID)). MSID dataset values 97.67%, 95.19%, 97.96%, 95.11%, 96.58%, 95.10%, 90.93% and 96.65% are achieved concerning accuracy, sensitivity, specificity, PPV, BAC, F1, IoU and ROC, respectively. While for the MPID dataset, values of 97.51%, 94.84%, 94.48%, 94.96%, 96.66%, 94.88%, 90.45% and 96.69% are achieved concerning accuracy, sensitivity, specificity, PPV, BAC, F1, IoU and ROC, respectively.