Software fault prediction algorithms predict which software components is likely to contain faults using machine learning techniques. Software diagnosis algorithm identify the faulty software components that caused a failure using model-based or spectrum based approaches. We show how software fault prediction algorithms can be used to improve software diagnosis. The resulting data-augmented diagnosis algorithm overcomes key problems in software diagnosis algorithms: ranking diagnoses and distinguishing between diagnoses with high probability and low probability. We demonstrate the efficiency of the proposed approach empirically on three open sources domains, showing significant increase in accuracy of diagnosis and efficiency of troubleshooting. These encouraging results suggests broader use of data-driven methods to complement and improve existing model-based methods.
The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Techniques, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, Resources, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography.
Nest is looking for a software engineer to join our ever growing embedded software team. At Nest you'll have to opportunity to shape the Consumer Electronic business, join a world class engineering team, and help revolutionize the next unloved device in our homes. You'll be responsible to architect, design, and implement the heart that makes our future products tick.
Our approach to automatic programming is based on reuse of generic algorithms through views. A generic algorithm performs some task, such as sorting a linked list of records, based on abstract descriptions of the data on which the program operates. A view describes how actual application data corresponds to the abstract data as used in the generic algorithm. Given a view, a generic algorithm can be specialized by a compilation process to produce a version of the algorithm that performs the algorithm directly on the application data.
When I started learning Machine Learning (ML) two years back, I had many questions around which algorithms to use, how to correlate it to datasets, etc. The answer depended on many factors like the size of data, expected output, and available computational resources. I was then introduced to the ML cheat sheets which acquainted me with the frequently used algorithms, packages, and functions.