Constructing Search Spaces for Search-Based Software Testing Using Neural Networks

A central requirement for any Search-Based Software Testing (SBST) technique is a convenient and meaningful fitness landscape. Whether one follows a targeted or a diversification driven strategy, a search landscape needs to be large, continuous, easy to construct and representative of the underlying property of interest. Constructing such a landscape is not a trivial task often requiring a significant manual effort by an expert. We present an approach for constructing meaningful and convenient fitness landscapes using neural networks (NN) – for targeted and diversification strategies alike. We suggest that output of an NN predictor can be interpreted as a fitness for a targeted strategy. The NN is trained on a corpus of execution traces and various properties of interest, prior to searching. During search, the trained NN is queried to predict an estimate of a property given an execution trace. The outputs of the NN form a convenient search space which is strongly representative of a number of properties. We believe that such a search space can be readily used for driving a search towards specific properties of interest. For a diversification strategy, we propose the use of an autoencoder; a mechanism for compacting data into an n-dimensional “latent” space. In it, datapoints are arranged according to the similarity of their salient features. We show that a latent space of execution traces possesses characteristics of a convenient search landscape: it is continuous, large and crucially, it defines a notion of similarity to arbitrary observations

Explaining cross-cultural service interactions in tourism with Shenkar’s Cultural Friction

In this paper, we commence a new dialogue on cross-cultural research in tourism. Using Shenkar’s (2001) metaphor of cultural friction as the analytical framework, we examine crosscultural service interactions between guests and service-providers in a luxury hotel. Cultural friction departs from, and extends, the notion of ‘cultural distance’, as it recognises asymmetry in social-economic conditions and considers the goals and the influence of control and power between the interacting parties. We use the Critical Incident Technique and Narrative Inquiry as the data collection technique and analytical approach respectively. The findings reveal that guests and service-providers use a number of strategies to exert power and gain control during their interactions, including subjective essentialism and stereotyping, to achieve their goals. The implications for tourism and hospitality management include providing cross-cultural sensitivity training to service-providers, ensuring a cultural-diverse employee composition, and to foster cross-cultural understanding amongst employees. We further suggest to develop strategies to facilitate effective cross-cultural service interactions based on evidence about cultural norms, expectations and behaviours from specific cultural groups. Further research is recommended to connect specific interactions between the interacting parties to examine whether the various strategies used leads to effective cross-cultural communication

MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters

Microbial Biotechnolog

MIBiG 3.0 : a community-driven effort to annotate experimentally validated biosynthetic gene clusters

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/