Software

About: Software is a research topic. Over the lifetime, 130577 publications have been published within this topic receiving 2028987 citations. The topic is also known as: computer software & computational tool.

Software engineering economics

Abstract: This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

MuxViz: A Tool for Multilayer Analysis and Visualization of Networks

Abstract: Multilayer relationships among entities and information about entities must be accompanied by the means to analyze, visualize, and obtain insights from such data. We present open-source software (muxViz) that contains a collection of algorithms for the analysis of multilayer networks, which are an important way to represent a large variety of complex systems throughout science and engineering. We demonstrate the ability of muxViz to analyze and interactively visualize multilayer data using empirical genetic, neuronal, and transportation networks. Our software is available at this https URL.

Toward deep learning software repositories

Abstract: Deep learning subsumes algorithms that automatically learn compositional representations The ability of these models to generalize well has ushered in tremendous advances in many fields such as natural language processing (NLP) Recent research in the software engineering (SE) community has demonstrated the usefulness of applying NLP techniques to software corpora Hence, we motivate deep learning for software language modeling, highlighting fundamental differences between state-of-the-practice software language models and connectionist models Our deep learning models are applicable to source code files (since they only require lexically analyzed source code written in any programming language) and other types of artifacts We show how a particular deep learning model can remember its state to effectively model sequential data, eg, Streaming software tokens, and the state is shown to be much more expressive than discrete tokens in a prefix Then we instantiate deep learning models and show that deep learning induces high-quality models compared to n-grams and cache-based n-grams on a corpus of Java projects We experiment with two of the models' hyper parameters, which govern their capacity and the amount of context they use to inform predictions, before building several committees of software language models to aid generalization Then we apply the deep learning models to code suggestion and demonstrate their effectiveness at a real SE task compared to state-of-the-practice models Finally, we propose avenues for future work, where deep learning can be brought to bear to support model-based testing, improve software lexicons, and conceptualize software artifacts Thus, our work serves as the first step toward deep learning software repositories

A strategic approach to software protection

Abstract: This paper demonstrates that there is a strategic reason why software firms have followed consumers' desire to drop software protection. We analyze software protection policies in a price-setting duopoly software industry selling differentiated software packages, where consumers' preference for particular software is affected by the number of other consumers who (legally or illegally) use the same software. Increasing network effects make software more attractive to consumers, thereby enabling firms to raise prices. However, it also generates a competitive effect resulting from feircer competition for market shares. We show that when network effects are strong, unprotecting is an equilibrium for a noncooperative industry.