Mining Software Repair Models for Reasoning on the Search Space of Automated Program Fixing

At this point, we are more interested in the “aftermarket” phase of the life of objects in the Soviet domestic economy, since once goods are brought into the home, they are

In order to compare them, each method is first described and then applied to an example of multitype branching processes: the evo- lution of the number of active buds for a

Patch construction is a key task in software development. In particular, it is central to the repair process when developers must engineer change operations for fixing the buggy

( 2009 ) ten years ago, the assessment of APR approaches in the literature attempts to pro- vide information on the number of bugs for which APR tool can generate a patch that makes

Learning-based repair approaches explore the advanced machine learning technique, especially deep learning technique, to boosting program repair [ 129 ]. The availability of

Because FixMiner considers code hunks as the unit for building Rich Edit Scripts, a given pattern may represent a repeating context (i.e., Shape pat- tern) or change (i.e., Action

When the method has no parameter, Args ← ∅. Given a buggy method, we consider all methods with similar method signatures as candidates for fix ingredients. In practice, we consider

• Connecting Program Synthesis and Reachability: Automatic Program Re- pair Using Test-Input Generation (2017) [134] creates a meta-program parametrized with parameters, encoding