Add to ORKGSoftware developers have to make several decisions, and sometimes experiments are needed. Since the experimental results depend on the conditions of the experiments, the decision might be inappropriate under unconsidered conditions. Software automatic tuning is a methodology for solving this problem: Software is equipped with functionality of doing experiments, analyzin
In software testing, it is often desirable to find test inputs that exercise specific program featur...
Statistical analysis is the tool of choice to turn data into information and then information into e...
We propose a novel family of Bayesian learning algorithms for online portfolio selection that overco...
Automatic tuning (auto-tuning) of software has emerged in recent years as a promising method that tr...
This paper proposes a Bayesian approach to the quantification of some of the subjectivity that is in...
The choice of an appropriate problem-solving method, from available methods, is a crucial skill for ...
Abstract—Big data applications are typically associated with systems involving large numbers of user...
The choice of the right problem-solving method, from available methods, is a crucial skill for exper...
The choice of the right problem-solving method, from available methods, is a crucial skill for exper...
The Bayesian approach to quantifying, analysing and reducing uncertainty in the application of compl...
Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating t...
Due to their complexity, modern systems expose many con-figuration parameters which users must ...
Software cybernetics is an emerging area that explores the interplay between software and control. T...
Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating t...
Abstract: The paper describes an approach to the formulation of the decision-making tasks via specif...
In software testing, it is often desirable to find test inputs that exercise specific program featur...
Statistical analysis is the tool of choice to turn data into information and then information into e...
We propose a novel family of Bayesian learning algorithms for online portfolio selection that overco...
Automatic tuning (auto-tuning) of software has emerged in recent years as a promising method that tr...
This paper proposes a Bayesian approach to the quantification of some of the subjectivity that is in...
The choice of an appropriate problem-solving method, from available methods, is a crucial skill for ...
Abstract—Big data applications are typically associated with systems involving large numbers of user...
The choice of the right problem-solving method, from available methods, is a crucial skill for exper...
The choice of the right problem-solving method, from available methods, is a crucial skill for exper...
The Bayesian approach to quantifying, analysing and reducing uncertainty in the application of compl...
Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating t...
Due to their complexity, modern systems expose many con-figuration parameters which users must ...
Software cybernetics is an emerging area that explores the interplay between software and control. T...
Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating t...
Abstract: The paper describes an approach to the formulation of the decision-making tasks via specif...
In software testing, it is often desirable to find test inputs that exercise specific program featur...
Statistical analysis is the tool of choice to turn data into information and then information into e...
We propose a novel family of Bayesian learning algorithms for online portfolio selection that overco...