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R.M. de Britto Heemskerk
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1
Testing is a major part of software development. Within testing often coverage requirements are used as a tool for quality assurance. But what code should be covered to reach the requirement is not clear.
To address this, we suggest using historic data to make these decisions more deliberate. In other words, we want to use machine learning to predict coverage.
Building upon previous research, we investigate how different approaches affect the performance of decision tree models. We did this using data from the Mozilla Firefox codebase. We focused in particular on the C/C++ code within there. Naively splitting training and test set and representing coverage per lines leads to best performance. Analysis showed that grouping coverage data based on basic blocks slightly lessened the predictive performance of the model. Meanwhile, splitting the data across the training set and test set based on their files appears to take away all predictive performance.
This study provides a new dataset for use in developer coverage prediction. It also introduces a new way of representing coverage data for developer coverage prediction, being basic-block coverage. And finally, gives insights on the effects of different coverage representations on decision trees. ...
To address this, we suggest using historic data to make these decisions more deliberate. In other words, we want to use machine learning to predict coverage.
Building upon previous research, we investigate how different approaches affect the performance of decision tree models. We did this using data from the Mozilla Firefox codebase. We focused in particular on the C/C++ code within there. Naively splitting training and test set and representing coverage per lines leads to best performance. Analysis showed that grouping coverage data based on basic blocks slightly lessened the predictive performance of the model. Meanwhile, splitting the data across the training set and test set based on their files appears to take away all predictive performance.
This study provides a new dataset for use in developer coverage prediction. It also introduces a new way of representing coverage data for developer coverage prediction, being basic-block coverage. And finally, gives insights on the effects of different coverage representations on decision trees. ...
Testing is a major part of software development. Within testing often coverage requirements are used as a tool for quality assurance. But what code should be covered to reach the requirement is not clear.
To address this, we suggest using historic data to make these decisions more deliberate. In other words, we want to use machine learning to predict coverage.
Building upon previous research, we investigate how different approaches affect the performance of decision tree models. We did this using data from the Mozilla Firefox codebase. We focused in particular on the C/C++ code within there. Naively splitting training and test set and representing coverage per lines leads to best performance. Analysis showed that grouping coverage data based on basic blocks slightly lessened the predictive performance of the model. Meanwhile, splitting the data across the training set and test set based on their files appears to take away all predictive performance.
This study provides a new dataset for use in developer coverage prediction. It also introduces a new way of representing coverage data for developer coverage prediction, being basic-block coverage. And finally, gives insights on the effects of different coverage representations on decision trees.
To address this, we suggest using historic data to make these decisions more deliberate. In other words, we want to use machine learning to predict coverage.
Building upon previous research, we investigate how different approaches affect the performance of decision tree models. We did this using data from the Mozilla Firefox codebase. We focused in particular on the C/C++ code within there. Naively splitting training and test set and representing coverage per lines leads to best performance. Analysis showed that grouping coverage data based on basic blocks slightly lessened the predictive performance of the model. Meanwhile, splitting the data across the training set and test set based on their files appears to take away all predictive performance.
This study provides a new dataset for use in developer coverage prediction. It also introduces a new way of representing coverage data for developer coverage prediction, being basic-block coverage. And finally, gives insights on the effects of different coverage representations on decision trees.
Intention aware routing system is a route-planning algorithm for electric vehicles that minimizes overall travel time by taking into consideration congestion at charging stations. This thesis extends this algorithm to allow choices to be made based on prices at charging stations. The goal of this thesis is to find a way to minimize maximum congestion while maximizing overall profit across the stations. To achieve this an optimal price has to be calculated. To this end, a formula is devised and applied to several graphs.
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Intention aware routing system is a route-planning algorithm for electric vehicles that minimizes overall travel time by taking into consideration congestion at charging stations. This thesis extends this algorithm to allow choices to be made based on prices at charging stations. The goal of this thesis is to find a way to minimize maximum congestion while maximizing overall profit across the stations. To achieve this an optimal price has to be calculated. To this end, a formula is devised and applied to several graphs.
Intention Aware Routing System is a route-planning algorithm for electric vehicles that minimizes overall travel time by taking into consideration congestion at charging stations. This paper extends this algorithm to allow choices to be made based on prices at charging stations. The goal of this paper is to find a way to minimize maximum congestion while maximizing overall profit across the stations.
...
Intention Aware Routing System is a route-planning algorithm for electric vehicles that minimizes overall travel time by taking into consideration congestion at charging stations. This paper extends this algorithm to allow choices to be made based on prices at charging stations. The goal of this paper is to find a way to minimize maximum congestion while maximizing overall profit across the stations.