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Presents a longitudinal field study which compares changes in perceptions of productivity and attendance behaviours for participants in a drug‐testing programme in a manufacturing firm in the mid‐western USA. Employee efficiency, productivity and absenteeism changes related to the implementation of the drug‐testing programme were measured by collecting and analysing actual organizational data. Data for each of the measures were collected for a 42‐month period, ranging from 18 months prior to the implementation of the programme to 24 months after the programme began. The attitudinal results provide, at best, circumspect support for the claims of drug‐testing proponents that the programme reduces drug abuse in the workplace and improves overall productivity. The performance results, however, document a positive and substantial impact of drug‐testing initiative on employee productivity and absenteeism rates.

Testing is one of the indispensable activities in software development and is being adopted as an independent course by software engineering (SE) departments at universities worldwide. The purpose of this paper is to carry out an investigation of the performance of learners about testing, given the tendencies in the industry and motivation caused by the unavailability of similar studies in software testing field.

This study is based on the data collected over three years (between 2012 and 2014) from students taking the software testing course. The course is included in the second year of undergraduate curriculum for the bachelor of engineering (SE).

It has been observed that, from the performance perspective, automated testing outperforms structural and functional testing techniques, and that a strong correlation exists among these three approaches. Moreover, a strong programming background does help toward further success in structural and automated testing, but has no effect on functional testing. The results of different teaching styles within the course are also presented together with an analysis exploring the relationship between students’ gender and success in the software testing course, revealing that there is no difference in terms of performance between male and female students in the course. Moreover, it is advisable to introduce teaching concepts one at a time because students find it difficult to grasp the ideas otherwise.

These findings are based on the analysis conducted using three years of data collected while teaching a course in testing. Obviously, there are some limitations to this study. For example, student’s strength in programming is calculated using the score of C programming courses taken in previous year/semester. Such scores may not reflect their current level of programming knowledge. Furthermore, attempt was made to ensure that the exercises given for different testing techniques have similar difficulty level to guarantee that the difference in success between these testing techniques is due to the inherent complexity of the technique itself and not because of different exercises. Still, there is small probability that a certain degree of change in success may be due to the difference in the difficulty levels of the exercises. As such, it is obviously premature to consider the present results as final since there is a lack of similar type of studies, with which the authors can compare the results. Therefore, more work needs to be done in different settings to draw sound conclusions in this respect.

Although there are few studies (see e.g. Chan et al., 2005; Garousi and Zhi, 2013; Ng et al., 2004) exploring the preference of testers over distinct software testing techniques in the industry, there appears to be no paper comparing the preferences and performances of learners in terms of different testing techniques.

Presents a longitudinal field study which compares changes in perceptions of productivity and attendance behaviours for participants in a drug‐testing programme in a manufacturing firm in the mid‐western USA. Employee efficiency, productivity and absenteeism changes related to the implementation of the drug‐testing programme were measured by collecting and analysing actual organizational data. Data for each of the measures were collected for a 42‐month period, ranging from 18‐months prior to the implementation of the programme to 24‐months after the programme began. The attitudinal results provide, at best, circumspect support for the claims of drug‐testing proponents that the programme reduces drug abuse in the workplace and improves overall productivity. The performance results, however, document a positive and substantial impact of drug testing initiative on employee productivity and absenteeism rates.

The purpose of this study is to reduce the number of mutations and, consequently, reduce the cost of mutation test. The results of related studies indicate that about 40% of injected faults (mutants) in the source code are effect-less (equivalent). Equivalent mutants are one of the major costs of mutation testing and the identification of equivalent and effect-less mutants has been known as an undecidable problem.

In a program with n branch instructions (if instruction) there are 2n execution paths (test paths) that the data and codes into each of these paths can be considered as a target of mutation. Given the role and impact of data in a program, some of data and codes propagates the injected mutants more likely to the output of the program. In this study, firstly the error-propagation rate of the program data is quantified using static analysis of the program control-flow graph. Then, the most error-propagating test paths are identified by the proposed heuristic algorithm (Genetic Algorithm [GA]). Data and codes with higher error-propagation rate are only considered as the strategic locations for the mutation testing.

In order to evaluate the proposed method, an extensive series of mutation testing experiments have been conducted on a set of traditional benchmark programs using MuJava tool set. The results depict that the proposed method reduces the number of mutants about 24%. Also, in the corresponding experiments, the mutation score is increased about 5.6%. The success rate of the GA in finding the most error-propagating paths of the input programs is 99%. On average, only 7.46% of generated mutants by the proposed method are equivalent. Indeed, 92.54% of generated mutants are non-equivalent.

The main contribution of this study is as follows: Proposing a set of equations to measure the error-propagation rate of each data, basic-block and execution path of a program. Proposing a genetic algorithm to identify a most error-propagating path of program as locations of mutations. Developing an efficient mutation-testing framework that mutates only the strategic locations of a program identified by the proposed genetic algorithms. Reducing the time and cost of mutation testing by reducing the equivalent mutants.

States that management faces a dilemma when it rightly desires to create and maintain a drug‐free work environment. Addresses the need to test employees to ensure a safe and productive workplace while ensuring that the testing procedures are neither demeaning to employees or viewed as an invasion of privacy. Recommends the establishment of a five‐step process. States that asking and answering the right questions before setting policy in this sensitive human resources area is essential. The framework presented is designed to assist management in planning for the development of a drug testing programme, responsive to the needs of the organization and its employees.

For delivering high-quality software applications, proper testing is required. A software test will function successfully if it can find more software faults. The traditional method of assessing the quality and effectiveness of a test suite is mutation testing. One of the main drawbacks of mutation testing is its computational cost. The research problem of this study is the high computational cost of the mutation test. Reducing the time and cost of the mutation test is the main goal of this study.

With regard to the 80–20 rule, 80% of the faults are found in 20% of the fault-prone code of a program. The proposed method statically analyzes the source code of the program to identify the fault-prone locations of the program. Identifying the fault-prone (complex) paths of a program is an NP-hard problem. In the proposed method, a firefly optimization algorithm is used for identifying the most fault-prone paths of a program; then, the mutation operators are injected only on the identified fault-prone instructions.

The source codes of five traditional benchmark programs were used for evaluating the effectiveness of the proposed method to reduce the mutant number. The proposed method was implemented in Matlab. The mutation injection operations were carried out by MuJava, and the output was investigated. The results confirm that the proposed method considerably reduces the number of mutants, and consequently, the cost of software mutation-test.

The proposed method avoids the mutation of nonfault-prone (simple) codes of the program, and consequently, the number of mutants considerably is reduced. In a program with n branch instructions (if instruction), there are 2n execution paths (test paths) that the data and codes into each of these paths can be considered as a target of mutation. Identifying the error-prone (complex) paths of a program is an NP-hard problem. In the proposed method, a firefly optimization algorithm as a heuristic algorithm is used for identifying the most error-prone paths of a program; then, the mutation operators (faults) are injected only on the identified fault-prone instructions.

To explore the appropriateness of statistical significance testing to measure the practical, managerial significance of outcomes in marketing programmes.

An in‐depth analysis of SST's scientific roots is coupled with delineation of a set of general objectives of marketing‐programme measurement to identify the applicability limits of significance testing.

In particular, it is shown that the relatively well known sample‐size dependence of SST and its somewhat lesser known replicability, representativeness and impact fallacies can severely affect the robustness of significance tests. Statistical significance is not the same concept as practical significance.

Comprehensive discussion of principles and practice leads to a set of prescriptive usage recommendations, directed at the goal of establishing much‐needed applicability rules and limits for the use of significance‐testing methodologies in an applied marketing context.

This robust challenge to the efficacy of significance testing in marketing practice should be of interest to any marketing planner concerned with the collection and use of marketing intelligence.

Despite a considerable increase in drug testing within the criminal justice system (CJS) through schemes such as the drug interventions programme, research is equivocal about its added value, as a recent series of reports from the UK Drug Policy Commission highlighted. The role of drug testing needs to be clarified and its cost‐effectiveness confirmed through studies with comparative regimes. Any further expansion of drug testing within the CJS should be undertaken with caution.

This preliminary investigation has shown that the programme load method of testing provides more useful information than single load level tests enabling a more reliable estimate of a structural joint fatigue life to be obtained.

This article examines the literature related to the marginalization of social studies through the lens of elementary social studies teacher education. This study presents the case of two different states wherein one state, Virginia, tests social studies in elementary schools and another state, North Carolina, where social studies is not tested until middle school. The data gathered from both states were originally analyzed to shed light on the question of testing's effect on teacher preparation and subsequent curriculum enactment. Data collected from the study suggest that factors such as field experiences, programs of study, and methods instruction impact teacher education in elementary social studies in more important ways than student testing.