Towards a software evolution benchmark

Case-studies are extremely popular in rapidly evolving research disciplines such as software engineering because they allow for a quick but fair assessment of new techniques. Unfortunately, a proper experimental set-up is rarely the case: all too often case-studies are based on a single small toy-example chosen to favour the technique under study. Such lack of scientific rigor prevents fair evaluation and has disastrous consequences for the credibility of our field. In this paper, we propose to use a representative set of cases as a benchmark for comparing various techniques dealing with software evolution. We hope that this proposal will launch a consensus building process that eventually must lead to a scientifically sound validation method for researchers investigating reverse- and reengineering techniques

Effects of cognitive control training on the dynamics of (mal)adaptive emotion regulation in daily life

Cognitive control plays a key role in both adaptive emotion regulation, such as positive reappraisal, and maladaptive emotion regulation, such as rumination, with both strategies playing a major role in resilience and well-being. As a result, cognitive control training (CCT) targeting working memory functioning may have the potential to reduce maladaptive emotion regulation and increase adaptive emotion regulation. The current study explored the effects of CCT on positive reappraisal ability in a lab context, and deployment and efficacy of positive appraisal and rumination in daily life. A sample of undergraduates (n = 83) was allocated to CCT or an active control condition, performing 10 online training sessions over a period of 14 days. Effects on regulation of affective states in daily life were assessed using experience sampling over a 7-day posttraining period. Results revealed a positive association between baseline cognitive control and self-reported use of adaptive emotion regulation strategies, whereas maladaptive emotion regulation strategies showed a negative association. CCT showed transfer to working memory functioning on the dual n-back task. Overall, effects of CCT on emotion regulation were limited to reducing deployment of rumination in low positive affective states. However, we did not find beneficial effects on indicators of adaptive emotion regulation. These findings are in line with previous studies targeting maladaptive emotion regulation but suggest limited use in enhancing adaptive emotion regulation in a healthy sample

The Index-Based Subgraph Matching Algorithm with General Symmetries (ISMAGS):Exploiting Symmetry for Faster Subgraph Enumeration

Subgraph matching algorithms are used to find and enumerate specific interconnection structures in networks. By enumerating these specific structures/subgraphs, the fundamental properties of the network can be derived. More specifically in biological networks, subgraph matching algorithms are used to discover network motifs, specific patterns occurring more often than expected by chance. Finding these network motifs yields information on the underlying biological relations modelled by the network. In this work, we present the Index-based Subgraph Matching Algorithm with General Symmetries (ISMAGS), an improved version of the Index-based Subgraph Matching Algorithm (ISMA). ISMA quickly finds all instances of a predefined motif in a network by intelligently exploring the search space and taking into account easily identifiable symmetric structures. However, more complex symmetries (possibly involving switching multiple nodes) are not taken into account, resulting in superfluous output. ISMAGS overcomes this problem by using a customised symmetry analysis phase to detect all symmetric structures in the network motif subgraphs. These structures are then converted to symmetry-breaking constraints used to prune the search space and speed up calculations. The performance of the algorithm was tested on several types of networks (biological, social and computer networks) for various subgraphs with a varying degree of symmetry. For subgraphs with complex (multi-node) symmetric structures, high speed-up factors are obtained as the search space is pruned by the symmetry-breaking constraints. For subgraphs with no or simple symmetric structures, ISMAGS still reduces computation times by optimising set operations. Moreover, the calculated list of subgraph instances is minimal as it contains no instances that differ by only a subgraph symmetry. An implementation of the algorithm is freely available at https://github.com/mhoubraken/ISMAGS

The Index-Based Subgraph Matching Algorithm (ISMA): Fast Subgraph Enumeration in Large Networks Using Optimized Search Trees

Subgraph matching algorithms are designed to find all instances of predefined subgraphs in a large graph or network and play an important role in the discovery and analysis of so-called network motifs, subgraph patterns which occur more often than expected by chance. We present the index-based subgraph matching algorithm (ISMA), a novel tree-based algorithm. ISMA realizes a speedup compared to existing algorithms by carefully selecting the order in which the nodes of a query subgraph are investigated. In order to achieve this, we developed a number of data structures and maximally exploited symmetry characteristics of the subgraph. We compared ISMA to a naive recursive tree-based algorithm and to a number of well-known subgraph matching algorithms. Our algorithm outperforms the other algorithms, especially on large networks and with large query subgraphs. An implementation of ISMA in Java is freely available at http://sourceforge.net/projects/isma

Apps For Amsterdam

Recently an app contest was organized, much in the vain of the "Apps for Democracy" initiative in Washington DC, called "Apps For Amsterdam". This was a partnership between the City of Amsterdam, Waag Society (Media Lab), and Hack de Overheid (a coders' open data community initiative). In this field note we will explore the process, the enthusiasm, the difficulties and results of this initiative

Evolution Metrics

Since the famous statement "What is not measurable make measurable " of Galileo Galilei (1564 -- 1642) it has been a major goal in science to quantify observations as a way to understand and control the underlying causes. With the growing awareness that evolution is a key aspect of software, an increasing number of computer scientists is investigating how metrics can be applied to evolving software artifacts. This paper provides a classification of the various approaches that use metrics to understand and control the software evolution process, gives concrete examples for each of the approaches, and identifies topics that require further research. As such, we expect that this paper will stimulate this emerging research area