Quantitative Analysis of Fault and Failure Using Software Metrics

It is very complex to write programs that behave accurately in the program verification tools. Automatic mining techniques suffer from 902013;99% false positive rates, because manual specification writing is not easy. Because they can help with program testing, optimization, refactoring, documentation, and most importantly, debugging and repair. To concentrate on this problem, we propose to augment a temporal-property miner by incorporating code quality metrics. We measure code quality by extracting additional information from the software engineering process, and using information from code that is more probable to be correct as well as code that is less probable to be correct. When used as a preprocessing step for an existing specification miner, our technique identifies which input is most suggestive of correct program behaviour, which allows off-the-shelf techniques to learn the same number of specifications using only 45% of their original input

Automatic Animal Behavior Analysis: Opportunities for Combining Knowledge Representation with Machine Learning

Computational animal behavior analysis (CABA) is an emerging field which aims to apply AI techniques to support animal behavior analysis. The need for computational approaches which facilitate ‘objectivization’ and quantification of behavioral characteristics of animals is widely acknowledged within several animal-related scientific disciplines. State-of-the-art CABA approaches mainly apply machine learning (ML) techniques, combining it with approaches from computer vision and IoT. In this paper we highlight the potential applications of integrating knowledge representation approaches in the context of ML-based CABA systems, demonstrating the ideas using insights from an ongoing CABA project

LEARNING AND VISUALIZING MUSIC SPECIFICATIONS USING PATTERN GRAPHS

ABSTRACT We describe a system to learn and visualize specifications from song(s) in symbolic and audio formats. The core of our approach is based on a software engineering procedure called specification mining. Our procedure extracts patterns from feature vectors and uses them to build pattern graphs. The feature vectors are created by segmenting song(s) and extracting time and and frequency domain features from them, such as chromagrams, chord degree and interval classification. The pattern graphs built on these feature vectors provide the likelihood of a pattern between nodes, as well as start and ending nodes. The pattern graphs learned from a song(s) describe formal specifications that can be used for human interpretable quantitatively and qualitatively song comparison or to perform supervisory control in machine improvisation. We offer results in song summarization, song and style validation and machine improvisation with formal specifications

Parametric Trace Slicing

A program trace is obtained and events of the program trace are traversed. For each event identified in traversing the program trace, a trace slice of which the identified event is a part is identified based on the parameter instance of the identified event. For each trace slice of which the identified event is a part, the identified event is added to an end of a record of the trace slice. These parametric trace slices can be used in a variety of different manners, such as for monitoring, mining, and predicting