Test Prioritization Research Articles

A dynamic test prioritisation based on DU-chain coverage for regression testing

A dynamic test prioritisation based on DU-chain coverage for regression testing

High-throughput in Vitro Data To Inform Prioritization of Ambient Water Monitoring and Testing for Endocrine Active Chemicals.

The presence of industrial chemicals, consumer product chemicals, and pharmaceuticals is well documented in waters in the U.S. and globally. Most of these chemicals lack health-protective guidelines and many have been shown to have endocrine bioactivity. There is currently no systematic or national prioritization for monitoring waters for chemicals with endocrine disrupting activity. We propose ambient water bioactivity concentrations (AWBCs) generated from high throughput data as a health-based screen for endocrine bioactivity of chemicals in water. The U.S. EPA ToxCast program has screened over 1800 chemicals for estrogen receptor (ER) and androgen receptor (AR) pathway bioactivity. AWBCs are calculated for 110 ER and 212 AR bioactive chemicals using high throughput ToxCast data from in vitro screening assays and predictive pathway models, high-throughput toxicokinetic data, and data-driven assumptions about consumption of water. Chemical-specific AWBCs are compared with measured water concentrations in data sets from the greater Denver area, Minnesota lakes, and Oregon waters, demonstrating a framework for identifying endocrine bioactive chemicals. This approach can be used to screen potential cumulative endocrine activity in drinking water and to inform prioritization of future monitoring, chemical testing and pollution prevention efforts.

Code coverage differences of Java bytecode and source code instrumentation tools

Many software testing fields, like white-box testing, test case generation, test prioritization, and fault localization, depend on code coverage measurement. If used as an overall completeness measure, the minor inaccuracies of coverage data reported by a tool do not matter that much; however, in certain situations, they can lead to serious confusion. For example, a code element that is falsely reported as covered can introduce false confidence in the test. This work investigates code coverage measurement issues for the Java programming language. For Java, the prevalent approach to code coverage measurement is using bytecode instrumentation due to its various benefits over source code instrumentation. As we have experienced, bytecode instrumentation-based code coverage tools produce different results than source code instrumentation-based ones in terms of the reported items as covered. We report on an empirical study to compare the code coverage results provided by tools using the different instrumentation types for Java coverage measurement on the method level. In particular, we want to find out how much a bytecode instrumentation approach is inaccurate compared to a source code instrumentation method. The differences are systematically investigated both in quantitative (how much the outputs differ) and in qualitative terms (what the causes for the differences are). In addition, the impact on test prioritization and test suite reduction—a possible application of coverage measurement—is investigated in more detail as well.

Prioritizing test cases for regression techniques using metaheuristic techniques

Regression testing attempts to find new errors after some alterations have been done in the software. Previously run test cases are re-executed to check whether historical faults have re-emerged or to validate the functionality of the software. In the past, several methods have been analyzed for reducing the cost of regression testing, which includes regression test selection, regression test minimization and regression test prioritization. Test Suite Prioritization techniques orders the test cases so that all the test cases are executed on a priority basis. Artificial bee colony (ABC) algorithm is a very powerful and efficient algorithm influenced by the intelligent nature of bees. In this paper, we have applied the ABC algorithm for test suite prioritization and the comparison has been made with Genetic Algorithm and Cuscuta Search. From our experimental results, we can infer that results of Cuscuta Search are low as compared to ABC and GA for small test suites.

The case for viral load testing in adolescents in resource-limited settings.

IntroductionThe success of HIV treatment programmes globally has resulted in children with perinatally acquired HIV reaching adolescence in large numbers. The number of adolescents living with HIV is growing further due to persisting high HIV incidence rates among adolescents in low‐ and middle‐income settings, particularly in sub‐Saharan Africa. Although expanding access to HIV viral load monitoring is necessary to achieve the 90‐90‐90 targets across the HIV care continuum, implementation is incomplete. We discuss the rationale for prioritizing viral load monitoring among adolescents and the associated challenges.DiscussionAdolescents with HIV are a complex group to treat successfully due to extensive exposure to antiretroviral therapy for those with perinatally acquired HIV and the challenges in sustained medication adherence in this age group. Given the high risk of treatment failure among adolescents and the limited drug regimens available in limited resource settings, HIV viral load monitoring in adolescents could prevent unnecessary and costly switches to second‐line therapy in virologically suppressed adolescents. Because adolescents living with HIV may be heavily treatment experienced, have suboptimal treatment adherence, or may be on second or even third‐line therapy, viral load testing would allow clinicians to make informed decisions about increased counselling and support for adolescents together with the need to maintain or switch therapeutic regimens.ConclusionsGiven scarce resources, prioritization of viral load testing among groups with a high risk of virological failure may be required. Adolescents have disproportionately high rates of virological failure, and targeting this age group for viral load monitoring may provide valuable lessons to inform broader scale‐up.

Three-dimensional patterning in biomedicine: Importance and applications in neuropharmacology.

Nature manufactures biological systems in three dimensions with precisely controlled spatiotemporal profiles on hierarchical length and time scales. In this article, we review 3D patterning of biological systems on synthetic platforms for neuropharmacological applications. We briefly describe 3D versus 2D chemical and topographical patterning methods and their limitations. Subsequently, an overview of introducing a third dimension in neuropharmacological research with delineation of chemical and topographical roles is presented. Finally, toward the end of this article, an explanation of how 3D patterning has played a pivotal role in relevant fields of neuropharmacology to understand neurophysiology during development, normal health, and disease conditions is described. The future prospects of organs-on-a--like devices to mimic patterned blood-brain barrier in the context of neurotherapeutic discovery and development for the prioritization of lead candidates, membrane potential, and toxicity testing are also described. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 1369-1382, 2018.

Status epilepticus in dogs and cats, part 1: etiopathogenesis, epidemiology, and diagnosis.

To review current knowledge of the etiopathogenesis, diagnosis, and consequences of status epilepticus (SE) in veterinary patients. Human and veterinary literature, including clinical and laboratory research and reviews. Status epilepticus is a common emergency in dogs and cats, and may be the first manifestation of a seizure disorder. It results from the failure of termination of an isolated seizure. Multiple factors are involved in SE, including initiation and maintenance of neuronal excitability, neuronal network synchronization, and brain microenvironmental contributions to ictogenesis. Underlying etiologies of epilepsy and SE in dogs and cats are generally classified as genetic (idiopathic), structural-metabolic, or unknown. Diagnosis of convulsive SE is usually made based on historical information and the nature of the seizures. Patient specific variables, such as the history, age of seizure onset, and physical and interictal neurological examination findings can help hone the rule out list, and are used to guide selection and prioritization of diagnostic tests. Electroencephalographic monitoring is routinely used in people to diagnose SE and guide patient care decisions, but is infrequently performed in veterinary medicine. Nonconvulsive status epilepticus has been recognized in veterinary patients; routine electroencephalography would aid in the diagnosis of this phenomenon in dogs and cats. Status epilepticus is a medical emergency that can result in life-threatening complications involving the brain and systemic organs. Status epilepticus often requires comprehensive diagnostic testing, treatment with multiple anticonvulsant agents, and intensive supportive care.

Tumor Sequencing and Patient-Derived Xenografts in the Neoadjuvant Treatment of Breast Cancer.

Background: Breast cancer patients with residual disease after neoadjuvant chemotherapy (NAC) have increased recurrence risk. Molecular characterization, knowledge of NAC response, and simultaneous generation of patient-derived xenografts (PDXs) may accelerate drug development. However, the feasibility of this approach is unknown.Methods: We conducted a prospective study of 140 breast cancer patients treated with NAC and performed tumor and germline sequencing and generated patient-derived xenografts (PDXs) using core needle biopsies. Chemotherapy response was assessed at surgery.Results: Recurrent “targetable” alterations were not enriched in patients without pathologic complete response (pCR); however, upregulation of steroid receptor signaling and lower pCR rates (16.7%, 1/6) were observed in triple-negative breast cancer (TNBC) patients with luminal androgen receptor (LAR) vs basal subtypes (60.0%, 21/35). Within TNBC, TP53 mutation frequency (75.6%, 31/41) did not differ comparing basal (74.3%, 26/35) and LAR (83.3%, 5/6); however, TP53 stop-gain mutations were more common in basal (22.9%, 8/35) vs LAR (0.0%, 0/6), which was confirmed in The Cancer Genome Atlas and British Columbia data sets. In luminal B tumors, Ki-67 responses were observed in tumors that harbored mutations conferring endocrine resistance (p53, AKT, and IKBKE). PDX take rate (27.4%, 31/113) varied according to tumor subtype, and in a patient with progression on NAC, sequencing data informed drug selection (olaparib) with in vivo antitumor activity observed in the primary and resistant (postchemotherapy) PDXs.Conclusions: In this study, we demonstrate the feasibility of tumor sequencing and PDX generation in the NAC setting. “Targetable” alterations were not enriched in chemotherapy-resistant tumors; however, prioritization of drug testing based on sequence data may accelerate drug development.

Predicting Severity of Disease-Causing Variants.

Most diseases, including those of genetic origin, express a continuum of severity. Clinical interventions for numerous diseases are based on the severity of the phenotype. Predicting severity due to genetic variants could facilitate diagnosis and choice of therapy. Although computational predictions have been used as evidence for classifying the disease relevance of genetic variants, special tools for predicting disease severity in large scale are missing. Here, we manually curated a dataset containing variants leading to severe and less severe phenotypes and studied the abilities of variation impact predictors to distinguish between them. We found that these tools cannot separate the two groups of variants. Then, we developed a novel machine-learning-based method, PON-PS (http://structure.bmc.lu.se/PON-PS), for the classification of amino acid substitutions associated with benign, severe, and less severe phenotypes. We tested the method using an independent test dataset and variants in four additional proteins. For distinguishing severe and nonsevere variants, PON-PS showed an accuracy of 61% in the test dataset, which is higher than for existing tolerance prediction methods. PON-PS is the first generic tool developed for this task. The tool can be used together with other evidence for improving diagnosis and prognosis and for prioritization of preventive interventions, clinical monitoring, and molecular tests.

Evaluating the zebrafish embryo toxicity test for pesticide hazard screening.

Given the numerous chemicals used in society, it is critical to develop tools for accurate and efficient evaluation of potential risks to human and ecological receptors. Fish embryo acute toxicity tests are 1 tool that has been shown to be highly predictive of standard, more resource-intensive, juvenile fish acute toxicity tests. However, there is also evidence that fish embryos are less sensitive than juvenile fish for certain types of chemicals, including neurotoxicants. The utility of fish embryos for pesticide hazard assessment was investigated by comparing published zebrafish embryo toxicity data from pesticides with median lethal concentration 50% (LC50) data for juveniles of 3 commonly tested fish species: rainbow trout, bluegill sunfish, and sheepshead minnow. A poor, albeit significant, relationship (r2 = 0.28; p < 0.05) was found between zebrafish embryo and juvenile fish toxicity when pesticides were considered as a single group, but a much better relationship (r2 = 0.64; p < 0.05) when pesticide mode of action was factored into an analysis of covariance. This discrepancy is partly explained by the large number of neurotoxic pesticides in the dataset, supporting previous findings that commonly used fish embryo toxicity test endpoints are particularly insensitive to neurotoxicants. These results indicate that it is still premature to replace juvenile fish toxicity tests with embryo-based tests such as the Organisation for Economic Co-operation and Development Fish Embryo Acute Toxicity Test for routine pesticide hazard assessment, although embryo testing could be used with other screening tools for testing prioritization. Environ Toxicol Chem 2017;36:1221-1226. © 2016 SETAC.

Gravitational Bee Search Algorithm with Fuzzy Logic for Effective Test Suite Minimization and Prioritization

Objectives: Software testing is the significant part of software development and is essential to confirm the quality of the software. The test suites developed for this purpose can be used again and updated repeatedly as the software advances. Subsequently, novel test cases will be added to the test suite and because of that, the size of the test suite will become bigger. Moreover, the test suite becomes redundant. Thus executing/re-executing the large test suite consumes more time and also increases the cost of testing. Therefore, in order to minimize the cost and the time of testing, it is essential to minimize the test suite. Methods/Statistical Analysis: Thus, the focus of this paper is to minimize the test suite by discovering a group of test cases that gives the same or better coverage as the original test suite based on some condition. Finding: In this study, the minimization is achieved by using a Gravitational Bee Search (GBS) algorithm, this algorithm is derived by combining artificial bee colony and gravitational search algorithms. Then, the Fuzzy operation is applied for prioritization to achieve efficient test suite. The algorithm searches for the optimum solution by calculating fitness values using coverage information. The search process is repetitive until a reduced test suite is identified. Application/ Improvement: The proposed algorithm is applied on an online ticket booking system and the results shows that the proposed system is approximately 15% to 20% more efficient than the existing system respective to the number of test cases and execution time.

Including Bioconcentration Kinetics for the Prioritization and Interpretation of Regulatory Aquatic Toxicity Tests of Highly Hydrophobic Chemicals.

Worldwide, regulations of chemicals require short-term toxicity data for evaluating hazards and risks of the chemicals. Current data requirements on the registration of chemicals are primarily based on tonnage and do not yet consider properties of chemicals. For example, short-term ecotoxicity data are required for chemicals with production volume greater than 1 or 10 ton/y according to REACH, without considering chemical properties. Highly hydrophobic chemicals are characterized by low water solubility and slow bioconcentration kinetics, which may hamper the interpretation of short-term toxicity experiments. In this work, internal concentrations of highly hydrophobic chemicals were predicted for standard acute ecotoxicity tests at three trophic levels, algae, invertebrate, and fish. As demonstrated by comparison with maximum aqueous concentrations at water solubility, chemicals with an octanol-water partition coefficient (Kow) greater than 106 are not expected to reach sufficiently high internal concentrations for exerting effects within the test duration of acute tests with fish and invertebrates, even though they might be intrinsically toxic. This toxicity cutoff was explained by the slow uptake, i.e., by kinetics, not by thermodynamic limitations. Predictions were confirmed by data entries of the OECD's screening information data set (SIDS) (n = 746), apart from a few exceptions concerning mainly organometallic substances and those with inconsistency between water solubility and Kow. Taking error propagation and model assumptions into account, we thus propose a revision of data requirements for highly hydrophobic chemicals with log Kow > 7.4: Short-term toxicity tests can be limited to algae that generally have the highest uptake rate constants, whereas the primary focus of the assessment should be on persistence, bioaccumulation, and long-term effects.

Cluster‐based test cases prioritization and selection technique for agile regression testing

AbstractRegression testing repeatedly executes test cases of previous builds to validate that the original features are not affected with any new changes. In recent years, regression testing has seen a remarkable progress with the increasing popularity of agile methods, which stress the central role of regression testing in maintaining software quality. The optimum case for regression testing in agile context is to run regression set at the end of each sprint and release, which requires a lot of cost and time. In this paper, we present an automated agile regression testing approach on both the sprints and release levels. The proposed approach addresses both weighted sprint test cases prioritization technique, which prioritizes test cases based on several parameters having real practical weight for testers, and Cluster‐based Release Test cases Selection technique that clusters user stories based on the similarity of covered modules to solve the scalability issue. Test cases are then selected based on issues logged for failed test cases using text mining techniques. The proposed approach achieves enhancement for both the prioritization and selection of test cases for agile regression testing. Copyright © 2016 John Wiley &amp; Sons, Ltd.

State-based models in regression test suite prioritization

Testing software products is very expensive and time consuming, especially for large software systems with extensive regression testing. During regression testing, a modified system is often re-tested using an existing test suite. Since test suites can be very large, testers are interested in detecting faults in the modified system as early as possible. Test prioritization tries to order test cases for execution in a way that increases the chances of the early detection of faults. Most of the existing test prioritization methods are based on the code of the system under test, but model-based test prioritization has been lately proposed. Most of the existing model-based test prioritization methods can be used only when models are modified during system maintenance. In this paper, we present model-based prioritization for a class of modifications for which models are not modified (only the source code is modified). After identifying the elements of the model related to the modified source code, information collected during the execution of the model is used to prioritize tests for execution. Here, we present and compare existing and new model-based test prioritization methods focused on this class of modifications. The major motivation for presenting these methods is to provide system developers with simple and yet effective test prioritization techniques for early fault detection. Statistical analysis of the empirical study, which compares the effectiveness of the presented methods in terms of early fault detection, show that compared to random ordering of test cases, model-based test prioritization significantly improve the effectiveness of test prioritization with respect to early fault detection.

Prioritizing manual test cases in rapid release environments

SummaryTest case prioritization is an important testing activity, in practice, specially for large scale systems. The goal is to rank the existing test cases in a way that they detect faults as soon as possible, so that any partial execution of the test suite detects the maximum number of defects for the given budget. Test prioritization becomes even more important when the test execution is time consuming, for example, manual system tests versus automated unit tests. Most existing test case prioritization techniques are based on code coverage, which requires access to source code. However, manual testing is mainly performed in a black‐box manner (manual testers do not have access to the source code). Therefore, in this paper, the existing test case prioritization techniques (e.g. diversity‐based and history‐based techniques) are examined and modified to be applicable on manual black‐box system testing. An empirical study on four older releases of desktop Firefox showed that none of the techniques were strongly dominating the others in all releases. However, when nine more recent releases of desktop Firefox, where the development has been moved from a traditional to a more agile and rapid release environment, were studied, a very significant difference between the history‐based approach and its alternatives was observed. The higher effectiveness of the history‐based approach compared with alternatives also held on 28 additional rapid releases of other Firefox projects – mobile Firefox and tablet Firefox. The conclusion of the paper is that test cases in rapid release environments can be very effectively prioritized for execution, based on their historical failure knowledge. In particular, it is the recency of historical knowledge that explains its effectiveness in rapid release environments rather than other changes in the process. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Scope-aided test prioritization, selection and minimization for software reuse

Abstract Software reuse can improve productivity, but does not exempt developers from the need to test the reused code into the new context. For this purpose, we propose here specific approaches to white-box test prioritization, selection and minimization that take into account the reuse context when reordering or selecting test cases, by leveraging possible constraints delimiting the new input domain scope. Our scope-aided testing approach aims at detecting those faults that under such constraints would be more likely triggered in the new reuse context, and is proposed as a boost to existing approaches. Our empirical evaluation shows that in test suite prioritization we can improve the average rate of faults detected when considering faults that are in scope, while remaining competitive considering all faults; in test case selection and minimization we can considerably reduce the test suite size, with small to no extra impact on fault detection effectiveness considering both in-scope and all faults. Indeed, in minimization, we improve the in-scope fault detection effectiveness in all cases.

Test case prioritization of build acceptance tests for an enterprise cloud application: An industrial case study

The use of cloud computing brings many new opportunities for companies to deliver software in a highly-customizable and dynamic way. One such paradigm, Software as a Service (SaaS), allows users to subscribe and unsubscribe to services as needed. While beneficial to both subscribers and SaaS service providers, failures escaping to the field in these systems can potentially impact an entire customer base. Build Acceptance Testing (BAT) is a black box technique performed to validate the quality of a SaaS system every time a build is generated. In BAT, the same set of test cases is executed simultaneously across many different servers, making this a time consuming test process. Since BAT contains the most critical use cases, it may not be obvious which tests to perform first, given that the time to complete all test cases across different servers in any given day may be insufficient. While all tests must be eventually run, it is critical to run those tests first which are likely to find failures. In this work, we ask if it is possible to prioritize BAT tests for improved time to fault detection and present several different approaches, each based on the services executed when running each BAT. In an empirical study on a production enterprise system, we first analyze the historical data from several months in the field, and then use that data to derive the prioritization order for the current development BATs. We then examine if the orders change significantly when we consider fault severity using a cost-based prioritization metric. We find that the prioritization order in which we run the tests does matter, and that the use of historical information is a good heuristic for this order. Prioritized tests have an increase in the rate of fault detection, with the average percent of faults detected (APFD) increasing from less than 0.30 to as high as 0.77 on a scale of zero to one. Although severity slightly changes which order performs best, we see that there are clusters of orderings, ones which improve time to early fault detection ones which don’t.

Cost-effective regression testing through Adaptive Test Prioritization strategies

Regression testing is an important part of the software development life cycle. It is also very expensive. Many different techniques have been proposed for reducing the cost of regression testing. However, research has shown that the effectiveness of different techniques varies under different testing environments and software change characteristics. In prior work, we developed strategies to investigate ways of choosing the most cost-effective regression testing technique for a particular regression testing session. In this work, we empirically study the existing strategies presented in prior work as well as develop two additional Adaptive Test Prioritization (ATP) strategies using fuzzy analytical hierarchy process (AHP) and the weighted sum model (WSM). We also provide a comparative study examining each of the ATP strategies presented to date. This research will provide researchers and practitioners with strategies to utilize in regression testing plans as well as provide data to use when deciding which of the strategies would best fit their testing needs. The empirical studies provided in this research show that utilizing these strategies can improve the cost-effectiveness of regression testing.

Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization.

Target-specific, mechanism-oriented in vitro assays post a promising alternative to traditional animal toxicology studies. Here we report the first comprehensive analysis of the Tox21 effort, a large-scale in vitro toxicity screening of chemicals. We test ∼10,000 chemicals in triplicates at 15 concentrations against a panel of nuclear receptor and stress response pathway assays, producing more than 50 million data points. Compound clustering by structure similarity and activity profile similarity across the assays reveals structure–activity relationships that are useful for the generation of mechanistic hypotheses. We apply structural information and activity data to build predictive models for 72 in vivo toxicity end points using a cluster-based approach. Models based on in vitro assay data perform better in predicting human toxicity end points than animal toxicity, while a combination of structural and activity data results in better models than using structure or activity data alone. Our results suggest that in vitro activity profiles can be applied as signatures of compound mechanism of toxicity and used in prioritization for more in-depth toxicological testing.

Genotype–phenotype Correlation in Children with Pheochromocytoma and Paraganglioma

ABSTRACTPheochromocytoma/paraganglioma (PPGL) have been reported to have germline mutations in more than 15 genes. PPGL diagnosed during childhood have the highest heritability (up to 80%). PPGL associated genes are classified into two clusters; cluster 1 (VHL, SDHx, EPAS1, PDH1, PDH2, FH, MDH2) and cluster 2 (RET, NF-1, TMEM127, MAX). Cluster 1 genes associated PPGL are norepinephrine secreting whereas cluster 2 genes associated PPGL are epinephrine secreting. In children with PPGL, VHL mutations are the most common followed by SDHB and SDHD. Bilateral PCC are frequent in patients with VHL mutations whereas extra-adrenal PGL are frequent in SDHx mutations. SDHB related PPGL are frequently malignant. Genetic testing should be performed in all children with PPGL and prioritization of genetic testing based on clinical characteristics (extra-paraganglial manifestations, location and number of PPGL, biochemical phenotype and metastasis) may be cost-effective.How to cite this articleSarathi V, Sabaretnam M, Shah N. Genotype–phenotype Correlation in Children with Pheochromocytoma and Paraganglioma. World J Endoc Surg 2016;8(3):208-211.