Related papers: A New Complete Class Complexity Metric
A variety of different performance metrics are commonly used in the machine learning literature for the evaluation of classification systems. Some of the most common ones for measuring quality of hard decisions are standard and balanced…
Quantum machine learning (QML) holds promise for accelerating pattern recognition, optimization, and data analysis, but the conditions under which it can truly outperform classical approaches remain unclear. Existing research often…
Metric learning seeks a transformation of the feature space that enhances prediction quality for the given task at hand. In this work we provide PAC-style sample complexity rates for supervised metric learning. We give matching lower- and…
Robustness is a fundamental pillar of Machine Learning (ML) classifiers, substantially determining their reliability. Methods for assessing classifier robustness are therefore essential. In this work, we address the challenge of evaluating…
This work aims at discussing the complexity aspect of software while demonstrating its relationship with security. Complexity is an essential part of software; however, numerous studies indicate that they increase the vulnerability of the…
We propose a measure of learning efficiency for non-finite state spaces. We characterize the complexity of a learning problem by the metric entropy of its state space. We then describe how learning efficiency is determined by this measure…
This chapter serves as an introduction to systems engineering focused on the broad issues surrounding realizing complex integrated systems. What is a system? We pose a number of possible definitions and perspectives, but leave open the…
Commit messages (CMs) are an essential part of version control. By providing important context in regard to what has changed and why, they strongly support software maintenance and evolution. But writing good CMs is difficult and often…
Automatic evaluation for sentence simplification remains a challenging problem. Most popular evaluation metrics require multiple high-quality references -- something not readily available for simplification -- which makes it difficult to…
This article is about a measurement analysis based approach to help software practitioners in managing the additional level complexities and variabilities in software product line applications. The architecture of the proposed approach i.e.…
In recent years, Software has become an indispensable part of every segment from simple Office Automation to Space Technology and E-mail to E-commerce. The evolution in Software architecture is always an open issue for researchers to…
We present an efficient parametric model checking (PMC) technique for the analysis of software performability, i.e., of the performance and dependability properties of software systems. The new PMC technique works by automatically…
In Software Engineering, early detection of architectural issues is key. It helps mitigate the risk of poor performance, and lowers the cost of repairing these issues. Metrics give a quick overview of the project which helps designers with…
The selection of the best classification algorithm for a given dataset is a very widespread problem. It is also a complex one, in the sense it requires to make several important methodological choices. Among them, in this work we focus on…
Learned Compression (LC) is the emerging technology for compressing image and video content, using deep neural networks. Despite being new, LC methods have already gained a compression efficiency comparable to state-of-the-art image…
Code completion is widely used by software developers to provide coding suggestions given a partially written code snippet. Apart from the traditional code completion methods, which only support single token completion at minimal positions,…
Recent studies have largely investigated the detection of class design anomalies. They proposed a large set of metrics that help in detecting those anomalies and in predicting the quality of class design. While those studies and the…
Classification with Costly Features (CwCF) is a classification problem that includes the cost of features in the optimization criteria. Individually for each sample, its features are sequentially acquired to maximize accuracy while…
Software engineering and information systems practices seek ultimately to create the flawless product. One of the tools used to improve the quality of software development is the use of metrics. In this paper, metrics retrieved from open…
Managing quality (such as service availability or process adherence) during the development, operation, and maintenance of software(-intensive) systems and services is a challenging task. Although many organizations need to define, control,…