Testing random effects in linear mixed‐effects models with serially correlated errors

In linear mixed-effects models, random effects are used to capture the heterogeneityand variability between individuals due to unmeasured covariates or unknown bio-logical differences. Testing for the need of random effects is a nonstandard problembecause it requires testing on the boundary of parameter space where the asymptoticchi-squared distribution of the classical tests such as likelihood ratio and score testsis incorrect. In the literature several tests have been proposed to overcome this diffi-culty, however all of these tests rely on the restrictive assumption of i.i.d. measurementerrors. The presence of correlated errors, which often happens in practice, makes test-ing random effects much more difficult. In this paper, we propose a permutation testfor random effects in the presence of serially correlated errors. The proposed test notonly avoids issues with the boundary of parameter space, but also can be used fortesting multiple random effects and any subset of them. Our permutation procedureincludes the permutation procedure in Drikvandi, Verbeke, Khodadadi, and PartoviNia (2013) as a special case when errors are i.i.d., though the test statistics are dif-ferent. We use simulations and a real data analysis to evaluate the performance of theproposed permutation test. We have found that random slopes for linear and quadratictime effects may not be significant when measurement errors are serially correlated

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

Hitchhikers Need Free Vehicles! Shared Repositories for Statistical Analysis in SBST

As a means for improving the maturity of the data analysis methods used in the search-based software testing field, this paper presents the need for shared repositories of well-documented statistical analysis code and replication data. In addition to explaining the benefits associated with using these repositories, the paper gives suggestions (e.g., the testing of analysis code) for improving the study of data arising from experiments with randomized algorithms

A review of electrical metering accuracy standards in the context of dynamic power quality conditions of the grid

Numerous changes in electrical grid schemes, like the inclusion of renewable energy, the rise of non-linear loads and the emergence of electric vehicle charging, increases variable power quality conditions of the grid. In this dynamic scenario where energy could flow in both directions and the waveforms could be highly distorted, accuracy becomes a crucial factor for the correct measurement of electrical energy and power values. Errors in the assessment of these values have significant ramifications for revenue, billing and/or control. This non-ideal power quality scenario produces an error in electricity meters, that is not yet well known since there is no standardised procedure to calibrate meters under typical or emerging distorted waveform conditions. Current standards relevant for revenue energy meters like EN 50470-3:2006 allows measurements error up to ±2.5% while local regulations could be even more permissive. In order to establish an electricity fair trade market and meet expectations from consumers and utilities, electricity meters should arguably comply with higher accuracy standards. In this paper, the pertinence and possible impact of including tests under distorted waveform conditions, as well as new accuracy requirements on standards applicable to electricity meters for billing purposes will be discussed

Challenges for smart electricity meters due to dynamic power quality conditions of the grid : a review

During the last few years, Smart Electricity Meters have been deployed in several countries all around the world, replacing the trustable Electromechanical meter and even other electronic meters. Since the early deployments, many concerns and complaints from customers which do not trust Smart Meters accuracy have appeared. As a result, researchers of different institutions have been testing electricity meters under distorted waveform conditions and proposing methods to calibrate such meters in a more representative real world operative conditions. Applicable accuracy standards and regulations indicate a maximum distortion factor of 3% of the sinusoidal waveform for voltage and current during the calibration, which is not representative of many modern dynamic power quality scenarios. New tests and recommendations have been issued by regulatory bodies, but they are still not mandatory for meters to be certified. With many changes upcoming in the near future for the electrical Smart Grid like the inclusion of renewables, increasing non-linear loads, electric charging vehicles and other emerging technologies, the power quality conditions of the grid is expected to be significantly affected. In this paper, a review of the current and upcoming challenges for the smart meters is presented

Automatic detection and removal of ineffective mutants for the mutation analysis of relational database schemas

Data is one of an organization’s most valuable and strategic assets. Testing the relational database schema, which protects the integrity of this data, is of paramount importance. Mutation analysis is a means of estimating the fault-finding “strength” of a test suite. As with program mutation, however, relational database schema mutation results in many “ineffective” mutants that both degrade test suite quality estimates and make mutation analysis more time consuming. This paper presents a taxonomy of ineffective mutants for relational database schemas, summarizing the root causes of ineffectiveness with a series of key patterns evident in database schemas. On the basis of these, we introduce algorithms that automatically detect and remove ineffective mutants. In an experimental study involving the mutation analysis of 34 schemas used with three popular relational database management systems—HyperSQL, PostgreSQL, and SQLite—the results show that our algorithms can identify and discard large numbers of ineffective mutants that can account for up to 24% of mutants, leading to a change in mutation score for 33 out of 34 schemas. The tests for seven schemas were found to achieve 100% scores, indicating that they were capable of detecting and killing all non-equivalent mutants. The results also reveal that the execution cost of mutation analysis may be significantly reduced, especially with “heavyweight” DBMSs like PostgreSQL

SchemaAnalyst: Search-Based Test Data Generation for Relational Database Schemas

Data stored in relational databases plays a vital role in many aspects of society. When this data is incorrect, the services that depend on it may be compromised. The database schema is the artefact responsible for maintaining the integrity of stored data. Because of its critical function, the proper testing of the database schema is a task of great importance. Employing a search-based approach to generate high-quality test data for database schemas, SchemaAnalyst is a tool that supports testing this key software component. This presented tool is extensible and includes both an evaluation framework for assessing the quality of the generated tests and full-featured documentation. In addition to describing the design and implementation of SchemaAnalyst and overviewing its efficiency and effectiveness, this paper coincides with the tool’s public release, thereby enhancing practitioners’ ability to test relational database schemas

Effects of Molybdenum Supplementation on Performance of Forage‐fed SteersReceiving High‐sulfur Water

There has been on‐going research in the area of the consumption of high‐sulfur (S) water by steers grazing rangeland as well as forage‐fed steers in a feedlot setting. During the summer of 2009, a trial was conducted on the effects of high‐S water in finishing steers supplemented with molybdenum (Mo). The main purpose of the research was to gather data that may aid in the formulation of a supplement to counteract the negative effects of high‐S water consumed by ruminant livestock species in areas where sulfur concentration in water sources is a risk to animal health and performance. The specific focus of this trial was to determine whether the feeding of supplemental Mo would improve animal health and performance by decreasing the formation of hydrogen sulfide gas (H2S) in the rumen. Yearling steers (n=96) were used for a 56‐d trial. The trial consisted of 3 treatment groups; a low‐S water group and two high‐S water groups. One high‐S water treatment group received the same pellet that the low‐S group was given and the other high‐S water treatment group received a pellet with supplemental Mo included. Rumen gas cap H2S was collected on d ‐1, 29 and 57. Weights were recorded on d ‐2, ‐1, 29, 56 and 57. There were no differences between treatments in water intake (P= 0.719), but feed intake was reduced in the steers receiving the supplemental Mo (P \u3c 0.001). There was a significant difference in ruminal H2S due to treatment (P= 0.014), with higher ruminal H2S in the steers receiving the supplemental Mo. Steers receiving the Mo supplement had lower ADG than steers in the other treatments (P= 0.009). Throughout the duration of the trial, two steers were removed from the trial due to advanced symptoms of sulfur‐induced PEM (sPEM) from the high‐S treatment with no supplemental M