Mutation Analysis of Relational Database Schemas

The schema is the key artefact used to describe the structure of a relational database, specifying how data will be stored and the integrity constraints used to ensure it is valid. It is therefore surprising that to date little work has addressed the problem of schema testing, which aims to identify mistakes in the schema early in software development. Failure to do so may lead to critical faults, which may cause data loss or degradation of data quality, remaining undetected until later when they will prove much more costly to fix. This thesis explores how mutation analysis – a technique commonly used in software testing to evaluate test suite quality – can be applied to evaluate data generated to exercise the integrity constraints of a relational database schema. By injecting faults into the constraints, modelling both faults of omission and commission, this enables the fault-finding capability of test suites generated by different techniques to be compared. This is essential to empirically evaluate further schema testing research, providing a means of assessing the effectiveness of proposed techniques. To mutate the integrity constraints of a schema, a collection of novel mutation operators are proposed and implementation described. These allow an empirical evaluation of an existing data generation approach, demonstrating the effectiveness of the mutation analysis technique and identifying a configuration that killed 94% of mutants on average. Cost-effective algorithms for automatically removing equivalent mutants and other ineffective mutants are then proposed and evaluated, revealing a third of mutation scores to be mutation adequate and reducing time taken by an average of 7%. Finally, the execution cost problem is confronted, with a range of optimisation strategies being applied that consistently improve efficiency, reducing the time taken by several hours in the best case and as high as 99% on average for one DBMS

A mutation testing framework for triggers in PostgreSQL

Trabajo Fin Máster en Ingeniería Informática, Facultad de Informática UCM, Departamento de Sistemas Informáticos y Computación, Curso 2020/2021.El uso de las pruebas de mutación ha acaparado mucha atención durante las últimas décadas como técnica para determinar la calidad de los conjuntos de pruebas utilizados durante el proceso de validación de sistemas. Las pruebas de mutación se basan en la incorporación de pequeños cambios sintácticos en el código para simular fallos que programadores experimentados podrían realizar durante la fase de desarrollo o de mantenimiento del sistema. En este trabajo se ha desarrollado un marco de pruebas de mutación para disparadores dentro del ámbito de las bases de datos. En este contexto, los disparadores son fragmentos de código que se ejecutan automáticamente cuando se producen determinadas acciones sobre las tablas a las que se encuentran asociados. Por una parte se ha definido un conjunto de operadores de mutación sobre cláusulas específicas de estos objetos. Por otra parte se ha automatizado la aplicación de la técnica de pruebas de mutación a disparadores diseñados en PostgreSQL. La herramienta desarrollada permite evaluar la calidad de diferentes conjuntos de pruebas para detectar los errores inducidos por los operadores de mutación así como compararlos.In the last decades the use of mutation tests has been playing a very important role in determining the quality of the test cases used during the system validation process. Mutation testing consists of inserting small syntactic changes into the code to simulate some bugs that experienced programmers might make during the development or maintenance phase of a system. In this work, a set of mutation tests for triggers has been done within the scope of databases. In this scenario, triggers are snippets of code which are automatically executed when certain actions occur on the tables to which they are asociated. On the one hand, a group of mutation operators has been defined on specific clauses of these objects. On the other hand, the application of the mutation testing technique to triggers has been automated to be used in PostgreSQL. The tool created allows us to evaluate the quality of different test cases to detect the errors caused by the mutation operators as well as to compare them.Depto. de Sistemas Informáticos y ComputaciónFac. de InformáticaTRUEunpu

Computational approaches to discovering differentiation genes in the peripheral nervous system of drosophila melanogaster

In the common fruit fly, Drosophila melanogaster, neural cell fate specification is triggered by a group of conserved transcriptional regulators known as proneural factors. Proneural factors induce neural fate in uncommitted neuroectodermal progenitor cells, in a process that culminates in sensory neuron differentiation. While the role of proneural factors in early fate specification has been described, less is known about the transition between neural specification and neural differentiation. The aim of this thesis is to use computational methods to improve the understanding of terminal neural differentiation in the Peripheral Nervous System (PNS) of Drosophila. To provide an insight into how proneural factors coordinate the developmental programme leading to neural differentiation, expression profiling covering the first 3 hours of PNS development in Drosophila embryos had been previously carried out by Cachero et al. [2011]. The study revealed a time-course of gene expression changes from specification to differentiation and suggested a cascade model, whereby proneural factors regulate a group of intermediate transcriptional regulators which are in turn responsible for the activation of specific differentiation target genes. In this thesis, I propose to select potentially important differentiation genes from the transcriptional data in Cachero et al. [2011] using a novel approach centred on protein interaction network-driven prioritisation. This is based on the insight that biological hypotheses supported by diverse data sources can represent stronger candidates for follow-up studies. Specifically, I propose the usage of protein interaction network data because of documented transcriptome-interactome correlations, which suggest that differentially expressed genes encode products that tend to belong to functionally related protein interaction clusters. Experimental protein interaction data is, however, remarkably sparse. To increase the informative power of protein-level analyses, I develop a novel approach to augment publicly available protein interaction datasets using functional conservation between orthologous proteins across different genomes, to predict interologs (interacting orthologs). I implement this interolog retrieval methodology in a collection of open-source software modules called Bio:: Homology::InterologWalk, the first generalised framework using web-services for “on-the- fly” interolog projection. Bio::Homology::InterologWalk works with homology data for any of the hundreds of genomes in Ensembl and Ensembgenomes Metazoa, and with experimental protein interaction data curated by EBI Intact. It generates putative protein interactions and optionally collates meta-data into a prioritisation index that can be used to help select interologs with high experimental support. The methodology proposed represents a significant advance over existing interolog data sources, which are restricted to specific biological domains with fixed underlying data sources often only accessible through basic web-interfaces. Using Bio::Homology::InterologWalk, I build interolog models in Drosophila sensory neurons and, guided by the transcriptome data, find evidence implicating a small set of genes in a conserved sensory neuronal specialisation dynamic, the assembly of the ciliary dendrite in mechanosensory neurons. Using network community-finding algorithms I obtain functionally enriched communities, which I analyse using an array of novel computational techniques. The ensuing datasets lead to the elucidation of a cluster of interacting proteins encoded by the target genes of one of the intermediate transcriptional regulators of neurogenesis and ciliogenesis, fd3F. These targets are validated in vivo and result in improved knowledge of the important target genes activated by the transcriptional cascade, suggesting a scenario for the mechanisms orchestrating the ordered assembly of the cilium during differentiation