Related papers: Computational Reproducibility of R Code Supplement…

Reproducibility is the ability of recreating identical binaries under pre-defined build environments. Due to the need of quality assurance and the benefit of better detecting attacks against build environments, the practice of reproducible…

Automatically generating compilable programs with (or without) natural language descriptions has always been a touchstone problem for computational linguistics and automated software engineering. Existing deep-learning approaches model code…

In recent decades the analysis of data has become increasingly computational. Correspondingly, this has changed how scientific and statistical work is shared. For example, it is now commonplace for underlying analysis code and data to be…

Like other types of computational research, modeling and simulation of biological processes (biomodels) is still largely communicated without sufficient detail to allow independent reproduction of results. But reproducibility in this area…

Reproducibility is an important feature of science; experiments are retested, and analyses are repeated. Trust in the findings increases when consistent results are achieved. Despite the importance of reproducibility, significant work is…

Reproducibility in the computational sciences has been stymied because of the complex and rapidly changing computational environments in which modern research takes place. While many will espouse reproducibility as a value, the challenge of…

In many fields of experimental science, papers that failed to replicate continue to be cited as a result of the poor discoverability of replication studies. As a first step to creating a system that automatically finds replication studies…

Establishing a docker-based replicability infrastructure offers the community a great opportunity: measuring the run time of information retrieval systems. The time required to present query results to a user is paramount to the users…

Nolan and Temple Lang argue that "the ability to express statistical computations is an essential skill." A key related capacity is the ability to conduct and present data analysis in a way that another person can understand and replicate.…

Data analysis for scientific experiments and enterprises, large-scale simulations, and machine learning tasks all entail the use of complex computational pipelines to reach quantitative and qualitative conclusions. If some of the activities…

Software developers often look for solutions to their code-level problems by submitting questions to technical Q&A websites like Stack Overflow (SO). They usually include example code segments with questions to describe the programming…

Containerization has revolutionized software deployment, with Docker leading the way due to its ease of use and consistent runtime environment. As Docker usage grows, optimizing Dockerfile performance, particularly by reducing rebuild time,…

Science depends on collaboration, result reproduction, and the development of supporting software tools. Each of these requires careful management of software versions. We present a unified model for installing, managing, and publishing…

Reproducibility has been consistently identified as an important component of scientific research. Although there is a general consensus on the importance of reproducibility along with the other commonly used 'R' terminology (i.e.,…

CONTEXT The R programming language has a huge and active community, especially in the area of statistical computing. Its interpreted nature allows for several interesting constructs, like the manipulation of functions at run-time, that…

This paper extends the notion of information processing capacity for non-independent input signals in the context of reservoir computing (RC). The presence of input autocorrelation makes worthwhile the treatment of forecasting and filtering…

Although it is possible to increase confidence in Free and Open Source Software (FOSS) by reviewing its source code, trusting code is not the same as trusting its executable counterparts. These are typically built and distributed by…

Reproducibility is a crucial aspect of scientific research that involves the ability to independently replicate experimental results by analysing the same data or repeating the same experiment. Over the years, many works have been proposed…

Data analysis in fundamental sciences nowadays is an essential process that pushes frontiers of our knowledge and leads to new discoveries. At the same time we can see that complexity of those analyses increases fast due to a)~enormous…

The increasing availability of cloud computing services for science has changed the way scientific code can be developed, deployed, and run. Many modern scientific workflows are capable of running on cloud computing resources. Consequently,…