DeltaImpactFinder: Assessing Semantic Merge Conflicts with Dependency Analysis

In software development, version control systems (VCS) provide branching and merging support tools. Such tools are popular among developers to concurrently change a code-base in separate lines and reconcile their changes automatically afterwards. However, two changes that are correct independently can introduce bugs when merged together. We call semantic merge conflicts this kind of bugs. Change impact analysis (CIA) aims at estimating the effects of a change in a codebase. In this paper, we propose to detect semantic merge conflicts using CIA. On a merge, DELTAIMPACTFINDER analyzes and compares the impact of a change in its origin and destination branches. We call the difference between these two impacts the delta-impact. If the delta-impact is empty, then there is no indicator of a semantic merge conflict and the merge can continue automatically. Otherwise, the delta-impact contains what are the sources of possible conflicts.Comment: International Workshop on Smalltalk Technologies 2015, Jul 2015, Brescia, Ital

Comparing fbeta-optimal with distance based merge functions

Merge functions informally combine information from a certain universe into a solution over that same universe. This typically results in a, preferably optimal, summarization. In previous research, merge functions over sets have been looked into extensively. A specic case concerns sets that allow elements to appear more than once, multisets. In this paper we compare two types of merge functions over multisets against each other. We examine both general properties as practical usability in a real world application

The Euclidean Algorithm for Generalized Minimum Distance Decoding of Reed-Solomon Codes

This paper presents a method to merge Generalized Minimum Distance decoding of Reed-Solomon codes with the extended Euclidean algorithm. By merge, we mean that the steps taken to perform the Generalized Minimum Distance decoding are similar to those performed by the extended Euclidean algorithm. The resulting algorithm has a complexity of O(n^2)

Bayes and empirical Bayes: do they merge?

Bayesian inference is attractive for its coherence and good frequentist properties. However, it is a common experience that eliciting a honest prior may be difficult and, in practice, people often take an {\em empirical Bayes} approach, plugging empirical estimates of the prior hyperparameters into the posterior distribution. Even if not rigorously justified, the underlying idea is that, when the sample size is large, empirical Bayes leads to "similar" inferential answers. Yet, precise mathematical results seem to be missing. In this work, we give a more rigorous justification in terms of merging of Bayes and empirical Bayes posterior distributions. We consider two notions of merging: Bayesian weak merging and frequentist merging in total variation. Since weak merging is related to consistency, we provide sufficient conditions for consistency of empirical Bayes posteriors. Also, we show that, under regularity conditions, the empirical Bayes procedure asymptotically selects the value of the hyperparameter for which the prior mostly favors the "truth". Examples include empirical Bayes density estimation with Dirichlet process mixtures.Comment: 27 page