Mining Sequences of Developer Interactions in Visual Studio for Usage Smells

In this paper, we present a semi-automatic approach for mining a large-scale dataset of IDE interactions to extract usage smells, i.e., inefficient IDE usage patterns exhibited by developers in the field. The approach outlined in this paper first mines frequent IDE usage patterns, filtered via a set of thresholds and by the authors, that are subsequently supported (or disputed) using a developer survey, in order to form usage smells. In contrast with conventional mining of IDE usage data, our approach identifies time-ordered sequences of developer actions that are exhibited by many developers in the field. This pattern mining workflow is resilient to the ample noise present in IDE datasets due to the mix of actions and events that these datasets typically contain. We identify usage patterns and smells that contribute to the understanding of the usability of Visual Studio for debugging, code search, and active file navigation, and, more broadly, to the understanding of developer behavior during these software development activities. Among our findings is the discovery that developers are reluctant to use conditional breakpoints when debugging, due to perceived IDE performance problems as well as due to the lack of error checking in specifying the conditional

Technical Debt Prioritization: State of the Art. A Systematic Literature Review

Background. Software companies need to manage and refactor Technical Debt issues. Therefore, it is necessary to understand if and when refactoring Technical Debt should be prioritized with respect to developing features or fixing bugs. Objective. The goal of this study is to investigate the existing body of knowledge in software engineering to understand what Technical Debt prioritization approaches have been proposed in research and industry. Method. We conducted a Systematic Literature Review among 384 unique papers published until 2018, following a consolidated methodology applied in Software Engineering. We included 38 primary studies. Results. Different approaches have been proposed for Technical Debt prioritization, all having different goals and optimizing on different criteria. The proposed measures capture only a small part of the plethora of factors used to prioritize Technical Debt qualitatively in practice. We report an impact map of such factors. However, there is a lack of empirical and validated set of tools. Conclusion. We observed that technical Debt prioritization research is preliminary and there is no consensus on what are the important factors and how to measure them. Consequently, we cannot consider current research conclusive and in this paper, we outline different directions for necessary future investigations

QuickLET : a web-based tool for estimating pollutant loads in a watershed

textWeb-based modeling tools for estimating pollutant loads in watersheds are few in literature. Those that are available for public access often require domain expertise, making them relevant mostly among environmental researchers. Aside from intensive efforts required to gather enormous amount of data, the complexity of the modeling tools themselves hinder their application among non-technical users. Consequently, environmental decision makers often rely on outside consultants to perform watershed assessments for them. This report presents the Quick Load Estimating Tool (QuickLET), a Web-based tool for estimating pollutant loads in watersheds across the contiguous US. QuickLET empowers users to visualize the effects of land use patterns, cultivated crops, and conservation practices through graphical representation. QuickLET implements an export coefficient approach for predicting the pollutant loads resulting in significant simplification of the estimating process.Computational Science, Engineering, and Mathematic

Model-Driven Development for Spring Boot Microservices

As microservices become more and more common, there is more interest in optimizing their development, making it faster, reducing the complexity and making it less error and bug prone. This work aims to explore how Model-driven Engineering (MDE) can be used to aid microservices’ development, especially Java microservices using Spring Boot. Firstly, this work starts by presenting its context, with a brief introduction to MDE and microservices, and how MDE can be used to facilitate microservices’ development. Then the State of the Art presents MDE base technologies and MDE-based solutions that can be used to develop microservices. Furthermore, a value analysis was also done, to explore the benefits of using MDE, and to define its value proposition. Lastly, a case study was elaborated following three different approaches, traditional development, DSL-based approach, and MDE-based tool approach, which were then compared by code quality and time required for development of the microservices. This work is especially interesting for someone who wants to develop Java microservices and wants to explore the different approaches and technologies to do so, namely by using MDE.À medida que os microserviços se tornam cada vez mais comuns, há mais interesse em otimizar seu desenvolvimento, otimizando o tempo de desenvolvimento, reduzindo a complexidade e tornando-o menos sujeito a erros e bugs. Este trabalho tem como objetivo explorar como é que Model-driven Engineering (MDE) pode ser usado para auxiliar o desenvolvimento de microserviços, especialmente microserviços Java usando Spring Boot. Em primeiro lugar, este trabalho começa apresentando seu contexto, com uma breve introdução ao MDE e microserviços, e explicando como é que MDE pode ser usado para facilitar o desenvolvimento de microserviços. Em seguida é apresentado o Estado da Arte, que apresenta tecnologias base de MDE e soluções baseadas em MDE (ferramentas), que podem ser utilizadas para desenvolver microserviços. Além disso, também foi feita uma análise de valor para explorar os benefícios do uso de MDE, e para definir sua proposta de valor. Por fim, foi elaborado um caso de estudo através de três abordagens diferentes, desenvolvimento tradicional, abordagem baseada em DSL, e abordagem utilizando uma ferramenta baseada em MDE, que foram então comparadas através da qualidade do código e pelo tempo necessário para o desenvolvimento dos microserviços. Este trabalho é especialmente interessante para quem quer desenvolver microsserviços Java e quer explorar as diferentes abordagens e tecnologias para o fazer, nomeadamente através da utilização de MDE

Increasing Software Reliability using Mutation Testing and Machine Learning

Mutation testing is a type of software testing proposed in the 1970s where program statements are deliberately changed to introduce simple errors so that test cases can be validated to determine if they can detect the errors. The goal of mutation testing was to reduce complex program errors by preventing the related simple errors. Test cases are executed against the mutant code to determine if one fails, detects the error and ensures the program is correct. One major issue with this type of testing was it became intensive computationally to generate and test all possible mutations for complex programs. This dissertation used machine learning for the selection of mutation operators that reduced the computational cost of testing and improved test suite effectiveness. The goals were to produce mutations that were more resistant to test cases, improve test case evaluation, validate then improve the test suite’s effectiveness, realize cost reductions by generating fewer mutations for testing and improving software reliability by detecting more errors. To accomplish these goals, experiments were conducted using sample programs to determine how well the reinforcement learning based algorithm performed with one live mutation, multiple live mutations and no live mutations. The experiments, measured by mutation score, were used to update the algorithm and improved accuracy for predictions. The performance was then evaluated on multiple processor computers. One key result from this research was the development of a reinforcement algorithm to identify mutation operator combinations that resulted in live mutants. During experimentation, the reinforcement learning algorithm identified the optimal mutation operator selections for various programs and test suite scenarios, as well as determined that by using parallel processing and multiple cores the reinforcement learning process for mutation operator selection was practical. With reinforcement learning the mutation operators utilized were reduced by 50 – 100%.In conclusion, these improvements created a ‘live’ mutation testing process that evaluated various mutation operators and generated mutants to perform real-time mutation testing while dynamically prioritizing mutation operator recommendations. This has enhanced the software developer’s ability to improve testing processes. The contributions of this paper’s research supported the shift-left testing approach, where testing is performed earlier in the software development cycle when error resolution is less costly

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research