Hybrid swarm and GA based approach for software test case selection

Being a crucial step and deciding factor for software reliability, software testing has evolved a long way and always attracted researchers due to various inherent challenges. The quality of a software application depends on the effectiveness of the testing carried out during development and maintenance phase. Testing is a crucial but time consuming activity that influences the overall cost of software development. Thus a minimal but efficient test suite selection is the need of the hour. This paper presents a hybrid technique based on swarm based search technique and GA (Genetic Algorithm) for selection of promising test cases to reduce the overall development cost and time of the application. We took component based software into consideration as they offer some inherent advantages over traditional software development paradigms

Aplicaciones de la teoría de la información y la inteligencia artificial al testing de software

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería de Sistemas lnformáticos y de Computación, leída el 4-05-2022Software Testing is a critical field for the software industry, as it has the main tools used to ensure the reliability of the produced software. Currently, mor then 50% of the time and resources for creating a software product are diverted to testing tasks, from unit testing to system testing. Moreover, there is a huge interest into automatising this field, as software gets bigger and the amount of required testing increases. however, software Testing is not only an industry oriented field; it is also a really interesting field with a noble goal (improving the reliability of software systems) that at the same tieme is full of problems to solve....Es Testing Software es un campo crítico para la industria del software, ya que éste contienen las principales herramientas que se usan para asegurar la fiabilidad del software producido. Hoy en día, más del 50% del tiempo y recursos necesarios para crear un producto software son dirigidos a tareas de testing, desde el testing unitario al testing a nivel de sistema. Más aún, hay un gran interés en automatizar este campo, ya que el software cada vez es más grande y la cantidad de testing requerido crece. Sin embargo, el Testing de Software no es solo un campo orientado a la industria; también es un campo muy interesante con un objetivo noble (mejorar la fiabilidad de los sistemas software) que al mismo tiempo está lleno de problemas por resolver...Fac. de InformáticaTRUEunpu

Test data generation : two evolutionary approaches to mutation testing

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Transformation by example

La transformation de modèles consiste à transformer un modèle source en un modèle cible conformément à des méta-modèles source et cible. Nous distinguons deux types de transformations. La première est exogène où les méta-modèles source et cible représentent des formalismes différents et où tous les éléments du modèle source sont transformés. Quand elle concerne un même formalisme, la transformation est endogène. Ce type de transformation nécessite généralement deux étapes : l’identification des éléments du modèle source à transformer, puis la transformation de ces éléments. Dans le cadre de cette thèse, nous proposons trois principales contributions liées à ces problèmes de transformation. La première contribution est l’automatisation des transformations des modèles. Nous proposons de considérer le problème de transformation comme un problème d'optimisation combinatoire où un modèle cible peut être automatiquement généré à partir d'un nombre réduit d'exemples de transformations. Cette première contribution peut être appliquée aux transformations exogènes ou endogènes (après la détection des éléments à transformer). La deuxième contribution est liée à la transformation endogène où les éléments à transformer du modèle source doivent être détectés. Nous proposons une approche pour la détection des défauts de conception comme étape préalable au refactoring. Cette approche est inspirée du principe de la détection des virus par le système immunitaire humain, appelée sélection négative. L’idée consiste à utiliser de bonnes pratiques d’implémentation pour détecter les parties du code à risque. La troisième contribution vise à tester un mécanisme de transformation en utilisant une fonction oracle pour détecter les erreurs. Nous avons adapté le mécanisme de sélection négative qui consiste à considérer comme une erreur toute déviation entre les traces de transformation à évaluer et une base d’exemples contenant des traces de transformation de bonne qualité. La fonction oracle calcule cette dissimilarité et les erreurs sont ordonnées selon ce score. Les différentes contributions ont été évaluées sur d’importants projets et les résultats obtenus montrent leurs efficacités.Model transformations take as input a source model and generate as output a target model. The source and target models conform to given meta-models. We distinguish between two transformation categories. Exogenous transformations are transformations between models expressed using different languages, and the whole source model is transformed. Endogenous transformations are transformations between models expressed in the same language. For endogenous transformations, two steps are needed: identifying the source model elements to transform and then applying the transformation on them. In this thesis, we propose three principal contributions. The first contribution aims to automate model transformations. The process is seen as an optimization problem where different transformation possibilities are evaluated and, for each possibility, a quality is associated depending on its conformity with a reference set of examples. This first contribution can be applied to exogenous as well as endogenous transformation (after determining the source model elements to transform). The second contribution is related precisely to the detection of elements concerned with endogenous transformations. In this context, we present a new technique for design defect detection. The detection is based on the notion that the more a code deviates from good practice, the more likely it is bad. Taking inspiration from artificial immune systems, we generate a set of detectors that characterize the ways in which a code can diverge from good practices. We then use these detectors to determine how far the code in the assessed systems deviates from normality. The third contribution concerns transformation mechanism testing. The proposed oracle function compares target test cases with a base of examples containing good quality transformation traces, and assigns a risk level based on the dissimilarity between the two. The traces help the tester understand the origin of an error. The three contributions are evaluated with real software projects and the obtained results confirm their efficiencies

The Development and Evaluation of Non-invasive Methods to Characterise the Disease States of Patients Utilising Selective Discrimination, Gas Chromatography-Mass Spectrometry and Chemometrics

The ‘smell’ of illness, disease or age has been known for many centuries, mainly created by volatile organic compounds (VOCs). Dogs were first reported to detect cancer in 2004. Increasingly, the profiles of VOCs are being utilised as non-invasive diagnostic methods. The aim of the thesis was to develop and evaluate the performance of analytical methods to characterise the disease states of patients utilising selective discrimination, gas chromatography-mass spectrometry (GC-MS) and chemometrics. The primary analytical technique investigated was GC-Time-of-Flight-MS coupled with headspace solid-phase microextraction (HS-SPME-GC-ToFMS). A robust and sensitive method was developed by optimisation of all sample analysis parameters and was applied to clinical samples from bladder and prostate cancer patients and those with hepatic disorders. This evidence was obtained by quantifying an internal standard, present in every sample and blank throughout the studies. Based on these findings, large numbers of clinical samples were analysed with confidence. Statistically significant mathematical models were developed in partnership with Cranfield University to classify the diseased state of samples and clinically relevant controls. PLS-DA was determined as the best classifier. The results from the HS-SPME-GC-ToFMS studies were highly promising. Bladder cancer gave a mean accuracy of >80 % and even low-grade tumours gave a sensitivity of 73 %, superior to urine cytology. Higher clinical performance was obtained in the prostate cancer study, with BPH distinguishable from cancer. Hepatic disorders were better again (>86 %). Preliminary studies on sepsis detection also showed promise. Several recommendations were made to enable significant clinical results in the future based on analytical rigour

A more realistic genetic algorithm

Genetic Algorithms (GAs) are loosely based on the concept of the natural cycle of reproduction with selective pressures favouring the individuals which are best suited to their environment (i.e. fitness function). However, there are many features of natural reproduction which are not replicated in GAs, such as population members taking some time to reach puberty. This thesis describes a programme of research which set out to investigate what would be the impact on the performance of a GA of introducing additional features which more closely replicate real life processes. The motivation for the work was curiosity. The approach has been tested using various standard test functions. The results are interesting and show that when compared with a Canonical GA, introducing various features such as the need to reach puberty before reproduction can occur and risk of illness can enhance the effectiveness of GAs in terms of the overall effort needed to find a solution. As the method simulating the nature rules, Cardiff Genetic Algorithm (CGA) introduces several features to each individual in programming modelling the real world. Each individual of the population is given a life-span and an age, the population size is allowed to vary; and rather than generations, the concept of time steps is introduced with each individual living for a number of time steps. An additional feature is also discussed involving multiple populations which have to compete for a limited resource which can be thought of as “water”. This together with an illness parameter and accidental death are used to study the behaviour of these population