An Empirical Exploration of Python Machine Learning API Usage

Machine learning is becoming an increasingly important part of many domains, both inside and outside of computer science. With this has come an increase in developers learning to write machine learning applications in languages like Python, using application programming interfaces (APIs) such as pandas and scikit-learn. However, given the complexity of these APIs, they can be challenging to learn, especially for new programmers. To create better tools for assisting developers with machine learning APIs, we need to understand how these APIs are currently used. In this thesis, we present a study of machine learning API usage in Python code in a corpus of machine learning projects hosted on Kaggle, a machine learning education and competition community site. We analyzed the most frequently used machine learning related libraries and the sub-modules of those libraries. Next, we studied the usage of different calls used by the developers to solve machine learning tasks. We also found information about which libraries are used in combination and discovered a number of cases where the libraries were imported but never used. We end by discussing potential next steps for further research and developments based on our work results

Optimizing compilation with preservation of structural code coverage metrics to support software testing

Code-coverage-based testing is a widely-used testing strategy with the aim of providing a meaningful decision criterion for the adequacy of a test suite. Code-coverage-based testing is also mandated for the development of safety-critical applications; for example, the DO178b document requires the application of the modified condition/decision coverage. One critical issue of code-coverage testing is that structural code coverage criteria are typically applied to source code whereas the generated machine code may result in a different code structure because of code optimizations performed by a compiler. In this work, we present the automatic calculation of coverage profiles describing which structural code-coverage criteria are preserved by which code optimization, independently of the concrete test suite. These coverage profiles allow to easily extend compilers with the feature of preserving any given code-coverage criteria by enabling only those code optimizations that preserve it. Furthermore, we describe the integration of these coverage profile into the compiler GCC. With these coverage profiles, we answer the question of how much code optimization is possible without compromising the error-detection likelihood of a given test suite. Experimental results conclude that the performance cost to achieve preservation of structural code coverage in GCC is rather low.Peer reviewedSubmitted Versio

Using formal methods to support testing

Formal methods and testing are two important approaches that assist in the development of high quality software. While traditionally these approaches have been seen as rivals, in recent years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing

Research On and Activities For Mathematically Gifted Students

This Topical Survey offers a brief overview of the current state of research on and activities for mathematically gifted students around the world. This is of interest to a broad readership, including educational researchers, research mathematicians, mathematics teachers, teacher educators, curriculum designers, doctoral students, and other stakeholders. It first discusses research concerning the nature of mathematical giftedness, including theoretical frameworks and methodologies that are helpful in identifying and/or creating mathematically gifted students, which is described in this section. It also focuses on research on and the development of mathematical talent and innovation in students, including connections between cognitive, social and affective aspects of mathematically gifted students. Exemplary teaching and learning practices, curricula and a variety of programs that contribute to the development of mathematical talent, gifts, and passion are described as well as the pedagogy and mathematics content suitable for educating pre-service and in-service teachers of mathematically gifted students. The final section provides a brief summary of the paper along with suggestions for the research, activities, and resources that should be available to support mathematically gifted students and their teachers, parents, and other stakeholders

Integrated TaaS platform for mobile development: Architecture solutions

Abstract—This paper examines the Testing-as-a-Service (TaaS) solutions in mobile development and proposes a universal TaaS platform: Cloud Testing of Mobile Systems (CTOMS). CTOMS is an integrated solution with a core infrastructure that enables the scaling of additional functionalities. The CTOMS’s benefits are explained, the architecture of the system is described in detail, and technical solutions are listed based on the feasibility study that resulted in creation of the first version of CTOMS for Android development. Index Terms—Testing-as-a-Service (TaaS), mobile application development, Android, integrated solution. I

Reinforced Condition/Decision Coverage (RC/DC): A New Criterion for Software Testing

A new Reinforced Condition/Decision Coverage (RC/DC) criterion for software testing is proposed. This criterion provides further development of the well-known Modified Condition/Decision Coverage (MC/DC) criterion and is more suitable for testing of safety-critical software. Formal definitions in the Z notation for RC/DC, as well as MC/DC, are presented. Specific examples of using of these criteria are considered and some features are formally proved

software during certification

An "asymmetric " approach to the assessment of safety-critica