24.04.16 - 27.04.16, Seminar 16172

Machine Learning for Dynamic Software Analysis: Potentials and Limits

Diese Seminarbeschreibung wurde vor dem Seminar auf unseren Webseiten veröffentlicht und bei der Einladung zum Seminar verwendet.

Motivation

Machine learning of software artifacts is an emerging area of interaction between the machine learning (ML) and software analysis (SA) communities. Increased productivity in software engineering hinges on the creation of new adaptive, scalable tools that can analyse large and continuously changing software systems. For example: agile software development using continuous integration and delivery can require new documentation models, static analyses, proofs and tests of millions of lines of code every 24 hours. These needs are being addressed by new SA techniques based on machine learning, such as learning-based software testing, invariant generation or code synthesis.

Machine learning is a powerful paradigm for SA that provides novel approaches to automating the generation of models and other essential artifacts. However, the ML and SA communities are traditionally separate, each with its own agenda. This Dagstuhl Seminar aims to bring together top researchers active in these two fields who can present the state of the art, and suggest new directions and collaborations for future research. We, the organisers, feel strongly that both communities have much to learn from each other, and the seminar will focus strongly on fostering a spirit of collaboration.

The three day program consists of an introductory day of overview talks by experts from the software analysis and machine learning communities. The aim is to outline the field and establish a scope and common framework. These will be followed on subsequent days by more specialist presentations and discussions. These specialist presentations and discussions will be organized into four themes:

  1. machine learning for model extraction,
  2. learning-based software testing,
  3. machine learning for systems integration, and
  4. applications of machine learning in non-traditional areas of software engineering.

There will also be an opportunity for tool demonstrations.