Virtual Mutation Analysis of Relational Database Schemas

Relational databases are a vital component of many modern soft- ware applications. Key to the definition of the database schema — which specifies what types of data will be stored in the database and the structure in which the data is to be organized — are integrity constraints. Integrity constraints are conditions that protect and preserve the consistency and validity of data in the database, preventing data values that violate their rules from being admitted into database tables. They encode logic about the application concerned, and like any other component of a software application, need to be properly tested. Mutation analysis is a technique that has been successfully applied to integrity constraint testing, seeding database schema faults of both omission and commission. Yet, as for traditional mutation analysis for program testing, it is costly to perform, since the test suite under analysis needs to be run against each individual mutant to establish whether or not it exposes the fault. One overhead incurred by database schema mutation is the cost of communicating with the database management system (DBMS). In this paper, we seek to eliminate this cost by performing mutation analysis virtually on a local model of the DBMS, rather than on an actual, running instance hosting a real database. We present an empirical evaluation of our virtual technique revealing that, across all of the studied DBMSs and schemas, the virtual method yields an average time saving of 51% over the baseline

Automated Search for Good Coverage Criteria: Moving from Code Coverage to Fault Coverage Through Search-Based Software Engineering

We propose to use Search-Based Software Engineering to automatically evolve coverage criteria that are well correlated with fault revelation, through the use of existing fault databases. We explain how problems of bloat and overfitting can be ameliorated in our approach, and show how this new method will yield insight into faults — as well as better guidance for Search-Based Software Testing

An empirical study on the use of defect prediction for test case prioritization

Test case prioritization has been extensively re-searched as a means for reducing the time taken to discover regressions in software. While many different strategies have been developed and evaluated, prior experiments have shown them to not be effective at prioritizing test suites to find real faults. This paper presents a test case prioritization strategy based on defect prediction, a technique that analyzes code features - such as the number of revisions and authors - to estimate the likelihood that any given Java class will contain a bug. Intuitively, if defect prediction can accurately predict the class that is most likely to be buggy, a tool can prioritize tests to rapidly detect the defects in that class. We investigated how to configure a defect prediction tool, called Schwa, to maximize the likelihood of an accurate prediction, surfacing the link between perfect defect prediction and test case prioritization effectiveness. Using 6 real-world Java programs containing 395 real faults, we conducted an empirical evaluation comparing this paper's strategy, called G-clef, against eight existing test case prioritization strategies. The experiments reveal that using defect prediction to prioritize test cases reduces the number of test cases required to find a fault by on average 9.48% when compared with existing coverage-based strategies, and 10.4% when compared with existing history-based strategies

Factors associated with posttraumatic stress symptoms in a prospective cohort of patients after abdominal sepsis: a nomogram

Objective: To determine to what extent patients who have survived abdominal sepsis suffer from symptoms of posttraumatic stress disorder (PTSD) and depression, and to identify potential risk factors for PTSD symptoms. Design and setting: PTSD and depression symptoms were measured using the Impact of Events Scale-Revised (IES-R), the Post-Traumatic Symptom Scale 10 (PTSS-10) and the Beck Depression Inventory II (BDI-II). Patients and participants: A total of 135 peritonitis patients were eligible for this study, of whom 107 (80%) patients completed the questionnaire. The median APACHE-II score was 14 (range 12-16), and 89% were admitted to the ICU. Measurements and results: The proportion of patients with "moderate" PTSD symptom scores was 28% (95% CI 20-37), whilst 10% (95% CI 6-17) of patients had "high" PTSD symptom scores. Only 5% (95% CI 2-12) of the patients expressed severe depression symptoms. Factors associated with increased PTSD symptoms in a multivariate ordinal regression model were younger age (0.74 per 10 years older, p = 0.082), length of ICU stay (OR = 1.4 per doubling of duration, p = 0.003) and having some (OR = 4.9, p = 0.06) or many (OR = 55.5, p < 0.001) traumatic memories of the ICU or hospital stay. Conclusion: As many as 38% of patients after abdominal sepsis report elevated levels of PTSD symptoms on at least one of the questionnaires. Our nomogram may assist in identifying patients at increased risk for developing symptoms of PTSD

SDF1-Induced Antagonism of Axonal Repulsion Requires Multiple G-Protein Coupled Signaling Components That Work in Parallel

SDF1 reduces the responsiveness of axonal growth cones to repellent guidance cues in a pertussis-toxin-sensitive, cAMP-dependent manner. Here, we show that SDF1's antirepellent effect can be blocked in embryonic chick dorsal root ganglia (DRGs) by expression of peptides or proteins inhibiting either Gαi, Gαq, or Gβγ. SDF1 antirepellent activity is also blocked by pharmacological inhibition of PLC, a common effector protein for Gαq. We also show that SDF1 antirepellent activity can be mimicked by overexpression of constitutively active Gαi, Gαq, or Gαs. These results suggest a model in which multiple G protein components cooperate to produce the cAMP levels required for SDF1 antirepellent activity

Guanosine stimulates neurite outgrowth in PC12 cells via activation of heme oxygenase and cyclic GMP

Undifferentiated rat pheochromocytoma (PC12) cells extend neurites when cultured in the presence of nerve growth factor (NGF). Extracellular guanosine synergistically enhances NGF-dependent neurite outgrowth. We investigated the mechanism by which guanosine enhances NGF-dependent neurite outgrowth. Guanosine administration to PC12 cells significantly increased guanosine 3-5-cyclic monophosphate (cGMP) within the first 24 h whereas addition of soluble guanylate cyclase (sGC) inhibitors abolished guanosine-induced enhancement of NGF-dependent neurite outgrowth. sGC may be activated either by nitric oxide (NO) or by carbon monoxide (CO). \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$N^{\omega }$$ \end{document}-Nitro-l-arginine methyl ester (l-NAME), a non-isozyme selective inhibitor of nitric oxide synthase (NOS), had no effect on neurite outgrowth induced by guanosine. Neither nNOS (the constitutive isoform), nor iNOS (the inducible isoform) were expressed in undifferentiated PC12 cells, or under these treatment conditions. These data imply that NO does not mediate the neuritogenic effect of guanosine. Zinc protoporphyrin-IX, an inhibitor of heme oxygenase (HO), reduced guanosine-dependent neurite outgrowth but did not attenuate the effect of NGF. The addition of guanosine plus NGF significantly increased the expression of HO-1, the inducible isozyme of HO, after 12 h. These data demonstrate that guanosine enhances NGF-dependent neurite outgrowth by first activating the constitutive isozyme HO-2, and then by inducing the expression of HO-1, the enzymes responsible for CO synthesis, thus stimulating sGC and increasing intracellular cGMP

A PKC-Dependent Recruitment of MMP-2 Controls Semaphorin-3A Growth-Promoting Effect in Cortical Dendrites

There is increasing evidence for a crucial role of proteases and metalloproteinases during axon growth and guidance. In this context, we recently described a functional link between the chemoattractive Sema3C and Matrix metalloproteinase 3 (MMP3). Here, we provide data demonstrating the involvement of MMP-2 to trigger the growth-promoting effect of Sema3A in cortical dendrites. The in situ analysis of MMP-2 expression and activity is consistent with a functional growth assay demonstrating in vitro that the pharmacological inhibition of MMP-2 reduces the growth of cortical dendrites in response to Sema3A. Hence, our results suggest that the selective recruitment and activation of MMP-2 in response to Sema3A requires a PKC alpha dependent mechanism. Altogether, we provide a second set of data supporting MMPs as effectors of the growth-promoting effects of semaphorins, and we identify the potential signalling pathway involved