The continuous growth of the mobile apps industry creates a competition among apps developers. To succeed, app developers must attract and retain users. User reviews provide a wealth of information about bugs to fix and features to add and can help app developers offer high-quality apps. However, apps may receive hundreds of unstructured reviews, which makes transforming them into change requests a difficult task. Approaches exist for analyzing and extracting topics from mobile app reviews, however, prioritizing these reviews has not gained much attention. In this study, we introduce the use of a consensus algorithm to help developers prioritize user reviews for the purpose of app evolution. We evaluate the usefulness of our approach and meaningfulness of its consensus rankings on four Android apps. We compare the rankings against reviews ranked by app developers manually and show that there is a strong correlation between the two (average Kendall rank correlation coefficient = 0.516). Thus, our approach can prioritize user reviews and help developers focus their time/effort on improving their apps instead of on identifying reviews to address in the next release.

In this paper, we study circular words arising in the development of equipment using shields in brachytherapy. This equipment has physical constraints that have to be taken into consideration. From an algorithmic point of view, the problem can be formulated as follows: Given a circular word, find a constrained circular word of the same length such that the Manhattan distance between these two words is minimal. We show that we can solve this problem in pseudo polynomial time (polynomial time in practice) using dynamic programming.

International audience

SUMMARY Software maintenance accounts for the largest part of the costs of any program. During maintenance activities, developers implement changes (sometimes simultaneously) on artifacts in order to fix bugs and to implement new requirements. To reduce this part of the costs, previous work proposed approaches to identify the artifacts of programs that change together. These approaches analyze historical data, mined from version control systems, and report change patterns, which lead at the causes, consequences, and actors of the changes to source code files. They also introduce so‐called change patterns that describe some typical change dependencies among files. In this paper, we introduce two novel change patterns: the asynchrony change pattern, corresponding to macro co‐changes (MC), that is, of files that co‐change within a large time interval (change periods) and the dephase change pattern, corresponding to dephase macro co‐changes (DC), that is, MC that always happens with the same shifts in time. We present our approach, that we named Macocha, to identify these two change patterns in large programs. We use the k‐nearest neighbor algorithm to group changes into change periods. We also use the Hamming distance to detect approximate occurrences of MC and DC. We apply Macocha and compare its performance in terms of precision and recall with UMLDiff (file stability) and association rules (co‐changing files) on seven systems: ArgoUML, FreeBSD, JFreeChart, Openser, SIP, XalanC, and XercesC developed with three different languages (C, C++, and Java). These systems have a size ranging from 532 to 1693 files, and during the study period, they have undergone 1555 to 23,944 change commits. We use external information and static analysis to validate (approximate) MC and DC found by Macocha. Through our case study, we show the existence and usefulness of these novel change patterns to ease software maintenance and, potentially, reduce related costs. Copyright © 2013 John Wiley & Sons, Ltd.

BACKGROUND: Inferring an evolutionary scenario for a gene family is a fundamental problem with applications both in functional and evolutionary genomics. The gene tree/species tree reconciliation approach has been widely used to address this problem, but mostly in a discrete parsimony framework that aims at minimizing the number of gene duplications and/or gene losses. Recently, a probabilistic approach has been developed, based on the classical birth-and-death process, including efficient algorithms for computing posterior probabilities of reconciliations and orthology prediction. RESULTS: In previous work, we described an algorithm for exploring the whole space of gene tree/species tree reconciliations, that we adapt here to compute efficiently the posterior probability of such reconciliations. These posterior probabilities can be either computed exactly or approximated, depending on the reconciliation space size. We use this algorithm to analyze the probabilistic landscape of the space of reconciliations for a real data set of fungal gene families and several data sets of synthetic gene trees. CONCLUSION: The results of our simulations suggest that, with exact gene trees obtained by a simple birth-and-death process and realistic gene duplication/loss rates, a very small subset of all reconciliations needs to be explored in order to approximate very closely the posterior probability of the most likely reconciliations. For cases where the posterior probability mass is more evenly dispersed, our method allows to explore efficiently the required subspace of reconciliations.

Change impact analysis aims at identifying software artefacts that are being affected by a change. It helps developers to assess their change efforts and perform more adequate changes. Several approaches have been proposed to aid in impact analysis. However, to the best of our knowledge, none of these approaches have been used to study the scope of changes in a program. We present a metaphor inspired by seismology and propose a mapping between the concepts of seismology and change propagation, to study the scope of change propagation. We perform three case studies on Pooka, Rhino, and Xerces-J to observe change propagation. We use ANOVA and Duncan statistical tests to assess the statistically significance of our observations, which show that changes propagate to a limited scope.

Impact analysis is crucial to make decisions among different alternative implementations and to anticipate future maintenance tasks. Several approaches were proposed to identify software artefacts being affected by a change. However, to the best of our knowledge, none of these approaches have been used to study the scope of changes in a program. Yet, this information would help developers assess their change efforts and perform more adequate changes. Thus, we present a metaphor inspired by seismology and propose a mapping between the concepts of seismology and software evolution. We show the applicability and usefulness of our metaphor using Rhino and Xerces-J.