The International Software Testing Qualifications Board (ISTQB) is a volunteer organization aiming to qualify software testing and quality assurance practitioners. It issues a certification scheme with many certification products for this subject matter. These products are based on the current state of practice and consist of a syllabus and a set of sample exam questions. They can also serve as a basis for academic courses in software testing. We recently revised and updated the Advanced Level Test Analyst Syllabus, which focuses on black-box test techniques. In this paper, we share our experience in developing the new syllabus. We describe the methodology for shaping the syllabus's content and selecting black-box techniques. We also show how the ISTQB framework is designed to fill the gap left by academic programs regarding black-box techniques by providing structured syllabi with clear learning objectives and business outcomes that align with industry demands.

Data flow coverage criteria are widely used in software testing, but there is almost no research on low-level data flow metrics as software defect predictors. Aims: We examine two such metrics in this context: dep-degree (DD) proposed by Beyer and Fararooy and a new data flow metric called dep-degree density (DDD).Method: We investigate the importance of DD and DDD in SDP models. We perform a correlation analysis to check if DD and DDD measure different aspects of the code than the well-known size, complexity, and documentation metrics. Finally, we perform experiments with five different classifiers on nine projects from the Unified Bug Dataset to compare the performance of the SDP models trained with and without data flow metrics. Results: 1) DD is noticeably correlated with many other code metrics, but DDD is not correlated or is very weakly correlated with other metrics considered in this study; 2) both DD and DDD are highly ranked in the feature importance analysis; 3) SDP models that use DD and DDD perform better than models that do not use data flow metrics. Conclusions: Data-flow metrics: DD and DDD can be valuable predictors in SDP models.

Urban transit network design (UTND) problem represents a challenge in designing routes, with a trade-off between serving passengers and operators benefits. In this study, a Memetic Algorithm (MA) is proposed to solve the UTND problem. The algorithm uses the hill climbing local search (HCLS) algorithm as an additional operator for Genetic Algorithm (GA) to improve routes construction during the global search. The proposed method consists of two phases. In the first phase, a set of solutions (transit network designs) is generated as an initial population for MA, where each solution consists of a set of routes. The predefined set of solutions satisfies the constraints such as route length or number of routes, and requirements like lack of loops, and that all nodes are covered by at least one route. In the second phase, the suggested Memetic Algorithm (MA) is used to generate all possible solutions from the predefined set. The MA tries to find the best structured solution that represents the flawless transit network. The proposed MA is applied on the widely examined benchmark problems: Mandl and Mumford networks. The experiment results show that the suggested MA provides significant improvements in terms of the direct trip percentage and average travel time compared to the previous studies.

Abstract Test-driven development (TDD) is a popular design approach used by the developers with testing being the important software development driving factor. On the other hand, mutation testing is considered one of the most effective testing techniques. However, there is not so much research on combining these two techniques together. In this paper, we propose a novel, hybrid approach called TDD+M which combines test-driven development process together with the mutation approach. The aim was to check whether this modified approach allows the developers to write a better quality code. We verify our approach by conducting a controlled experiment and we show that it achieves better results than the sole TDD technique. The experiment involved 22 computer science students split into eight groups. Four groups (TDD+M) were using our approach, the other four (TDD) – a normal TDD process. We performed a cross-experiment by measuring the code coverage and mutation coverage for each combination (code of group X, tests from group Y). The TDD+M tests achieved better coverage on the code from TDD groups than the TDD tests on their own code (53.3% vs. 33.5% statement coverage and 64.9% vs. 37.5% mutation coverage). The TDD+M tests also found more post-release defects in the TDD code than TDD tests in the TDD+M code. The experiment showed that adding mutation into the TDD process allows the developers to provide better, stronger tests and to write a better quality code.

In this paper, we present an application which aid an evaluation of the arterial diameter changes, based on ultrasound videos.The designed, implemented and verified algorithm uses the techniques of image processing, image analysis and pattern recognition, such as filtering, profile plot analysis and active contour method.Except determining the artery diameter over time it is also able to retrieve ECG from ultrasound video.The results obtained for both signals are synchronized, therefore it is possible to obtain the artery diameters in R wave points, which is a novel approach.Experiments were performed to assess the software validation by comparing the outcomes obtained with the evaluated algorithm with those manually-acquiredthe correlation is high.This is the first stage of the research in which we will build the cardiovascular predictive model to search for the new cardiovascular factors.

This paper proposes a classification framework based on simple classifiers organized in a tree‐like structure. It is observed that simple classifiers, even though they have high error rate, find similarities among classes in the problem domain. The authors propose to trade on this property by recognizing classes that are mistaken and constructing overlapping subproblems. The subproblems are then solved by other classifiers, which can be very simple, giving as a result a hierarchical classifier (HC). It is shown that HC, together with the proposed training algorithm and evaluation methods, performs well as a classification framework. It is also proven that such constructs give better accuracy than the root classifier it is built upon.