Gotowa bibliografia na temat „Genetic software engineering”

The role of software reliability and quality improvement is becoming more important than any other issues related to software development. To date, we have various techniques that give a prediction of software reliability like neural networks, fuzzy logic, and other evolutionary algorithms. A genetic algorithm has been explored for predicting software reliability. One of the important aspects of software quality is called software reliability, thus, software engineering is of a great place in the software industry. To increase the software reliability, it is mandatory that we must design a model that predicts the fault and error in the software program at early stages, rectify them and then increase the functionality of the program within a minimum time and in a low cost. There exist numerous algorithms that predict software errors such as the Genetic Algorithm, which has a very high ability to predict software bugs, failure and errors rather than any other algorithm. The main purpose of this paper is to predict software errors with so precise, less time-consuming and cost-effective methodology. The outcome of this research paper is showing that the rates of applied methods and strategies are more than 96 percent in ideal conditions.

The study focuses on one of the mobile-oriented environment competence components for software engineering (SE) students. It has been demonstrated that the implementation of the higher education standard for SE bachelors has generated a number of issues in terms of ensuring training quality, principally due to a lack of specification for both skills and learning outcomes. Designing a precise framework of professional competencies for SE bachelors is one method to overcome these issues. The research examines methods for developing K14 (the ability to participate in software design, including modeling (formal description) of its structure, behavior, and working processes), a critical particular professional competency for future software engineers. Recommendations for software design teaching techniques, learning content, modeling and design tools, and assessment of the level of formation of relevant competence are developed based on a historical and genetic review of software design training among SE students in the UK, USA, Canada, Australia, New Zealand, and Singapore. The industrial-style software design training (studio training) is used as an example. The transition from architectural to detailed design, as well as project implementation, are discussed. The study's future prospects include substantiating the third engineering component of SE – software construction (after requirements engineering and design engineering).

<p>Software industries face a common problem which is the maintenance cost of industrial software systems. There are lots of reasons behind this problem. One of the possible reasons is the high maintenance cost due to lack of knowledge about understanding the software systems that are too large, and complex. Software clustering is an efficient technique to deal with such kind of problems that arise from the sheer size and complexity of large software systems. Day by day the size and complexity of industrial software systems are rapidly increasing. So, it will be a challenging task for managing software systems. Software clustering can be very helpful to understand the larger software system, decompose them into smaller and easy to maintenance. In this paper, we want to give research direction in the area of software clustering in order to develop efficient clustering techniques for software engineering. Besides, we want to describe the most recent clustering techniques and their strength as well as weakness. In addition, we propose genetic algorithm based software modularization clustering method. The result section demonstrated that proposed method can effectively produce good module structure and it outperforms the state of the art methods. </p>

Software testing is essential for providing error-free software. It is a well-known fact that software testing is responsible for at least 50% of the total development cost. Therefore, it is necessary to automate and optimize the testing processes. Search-based software engineering is a discipline mainly focussed on automation and optimization of various software engineering processes including software testing. In this article, a novel approach of hybrid firefly and a genetic algorithm is applied for test data generation and selection in regression testing environment. A case study is used along with an empirical evaluation for the proposed approach. Results show that the hybrid approach performs well on various parameters that have been selected in the experiments.

In this work, we present a genetic algorithm to optimize predictive models used to estimate software quality characteristics. Software quality assessment is crucial in the software development field since it helps reduce cost, time and effort. However, software quality characteristics cannot be directly measured but they can be estimated based on other measurable software attributes (such as coupling, size and complexity). Software quality estimation models establish a relationship between the unmeasurable characteristics and the measurable attributes. However, these models are hard to generalize and reuse on new, unseen software as their accuracy deteriorates significantly. In this paper, we present a genetic algorithm that adapts such models to new data. We give empirical evidence illustrating that our approach out-beats the machine learning algorithm C4.5 and random guess.

For software developers, accurately forecasting software effort is very important. In the field of software engineering, it is also a very challenging topic. Miscalculated software effort in the early phase might cause a serious consequence. It not only effects the schedule, but also increases the cost price. It might cause a huge deficit. Because all of the different software development team has it is own way to calculate the software effort, the factors affecting project development are also varies. In order to solve these problems, this paper proposes a model which combines genetic algorithm (GA) with support vector machines (SVM). We can find the best parameter of SVM regression by the proposed model, and make more accurate prediction. During the research, we test and verify our model by using the historical data in COCOMO. We will show the results by prediction level (PRED) and mean magnitude of relative error (MMRE).

A proper understanding of biological processes requires an understanding of genetics and evolutionary mechanisms. The vast amounts of genetical information that can routinely be extracted with modern technology have so far not been accompanied by an equally extended understanding of the corresponding processes. The relationship between a single gene and the resulting properties, phenotype of an individual is rarely clear. This thesis addresses several computational challenges regarding identifying and assessing the effects of quantitative trait loci (QTL), genomic positions where variation is affecting a trait. The genetic information available for each individual is rarely complete, meaning that the unknown variable of the genotype in the loci modelled also needs to be addressed. This thesis contains the presentation of new tools for employing the information that is available in a way that maximizes the information used, by using hidden Markov models (HMMs), resulting in a change in algorithm runtime complexity from exponential to log-linear, in terms of the number of markers. It also proposes the introduction of inferred haplotypes to further increase the power to assess these unknown variables for pedigrees of related genetically diverse individuals. Modelling consequences of partial genetic information are also treated. Furthermore, genes are not directly affecting traits, but are rather expressed in the environment of and in concordance with other genes. Therefore, significant interactions can be expected within genes, where some combination of genetic variation gives a pronounced, or even opposite, effect, compared to when occurring separately. This thesis addresses how to perform efficient scans for multiple interacting loci, as well as how to derive highly accurate empirical significance tests in these settings. This is done by analyzing the mathematical properties of the objective function describing the quality of model fits, and reformulating it through a simple transformation. Combined with the presented prototype of a problem-solving environment, these developments can make multi-dimensional searches for QTL routine, allowing the pursuit of new biological insight.
eSSENCE

A typical irrigation scheduling problem is one of preparing a schedule to service a group of outlets. These outlets may either be serviced sequentially or simultaneously. This problem has an analogy with the classical earliness/tardiness machine scheduling problems in operations research (OR). In previous published work integer programme were used to solve such problems; however, such scheduling problems belong to a class of combinatorial problems known to be computationally demanding (NP-hard). This is widely reported in OR. Hence integer programme can only be used to solve relatively small problems usually in a research environment where considerable computational resources and time can be allocated to solve a single schedule. For practical applications meta-heuristics such as genetic algorithms, simulated annealing or tabu search methods need to be used. However as reported in the literature, these need to be formulated carefully and tested thoroughly. This thesis demonstrates how arranged-demand irrigation scheduling problems can be correctly formulated and solved using genetic algorithms (GA). By interpreting arrangeddemand irrigation scheduling problems as single or multi-machine scheduling problems, the wealth of information accumulated over decades in OR is capitalized on. The objective is to schedule irrigation supplies as close as possible to the requested supply time of the farmers to provide a better level of service. This is in line with the concept of Service Oriented Management (SOM), described as the central goal of irrigation modernization in recent literature. This thesis also emphasizes the importance of rigorous evaluation of heuristics such as GA. First, a series of single machine models is presented that models the warabandi (rotation) type of irrigation distribution systems, where farmers are supplied water sequentially. Next, the multimachine models are presented which model the irrigation water distribution systems where several farmers may be supplied water simultaneously. Two types of multimachine models are defined. The simple multimachine models where all the farmers are supplied with identical discharges and the complex multimachine models where the farmers are allowed to demand different discharges. Two different approaches i.e. the stream tube approach and the time block approach are used to develop the multimachine models. These approaches are evaluated and compared to determine the suitability of either for the irrigation scheduling problems, which is one of the significant contributions of this thesis. The multimachine models are further enhanced by incorporating travel times which is an important part of the surface irrigation canal system and need to be taken into account when determining irrigation schedules. The models presented in this thesis are unique in many aspects. The potential of GA for a wide range of irrigation scheduling problems under arranged demand irrigation system is fully explored through a series of computational experiments.

Many important traits in plants, animals and humans are quantitative, and most such traits are generally believed to be regulated by multiple genetic loci. Standard computational tools for analysis of quantitative traits use linear regression models for relating the observed phenotypes to the genetic composition of individuals in a population. However, using these tools to simultaneously search for multiple genetic loci is very computationally demanding. The main reason for this is the complex nature of the optimization landscape for the multidimensional global optimization problems that must be solved. This thesis describes parallel algorithms and implementation techniques for such optimization problems. The new computational tools will eventually enable genetic analysis exploiting new classes of multidimensional statistical models, potentially resulting in interesting results in genetics. We first describe how the algorithm used for global optimization in the standard, serial software is parallelized and implemented on a grid system. Then, we also describe a parallelized version of the more elaborate global optimization algorithm DIRECT and show how this can be deployed on grid systems and other loosely-coupled architectures. The parallel DIRECT scheme is further developed to exploit both coarse-grained parallelism in grid or clusters as well as fine-grained, tightly-coupled parallelism in multi-core nodes. The results show that excellent speedup and performance can be archived on grid systems and clusters, even when using a tightly-coupled algorithms such as DIRECT. Finally, a pilot implementation of a grid portal providing a graphical front-end for our code is implemented. After some further development, this portal can be utilized by geneticists for performing multidimensional genetic analysis of quantitative traits on a regular basis.

[EN] Synthetic Biology is an emerging interdisciplinary field that aims to apply the engineering principles of modularity, abstraction and standardization to genetic engineering. The nascent branch of Synthetic Biology devoted to plants, Plant Synthetic Biology (PSB), offers new breeding possibilities for crops, potentially leading to enhanced resistance, higher yield, or increased nutritional quality. To this end, the molecular tools in the PSB toolbox need to be adapted accordingly, to become modular, standardized and more precise. Thus, the overall objective of this Thesis was to adapt, expand and refine DNA assembly tools for PSB to enable the incorporation of functional specifications to the description of standard genetic elements (phytobricks) and to facilitate the construction of increasingly complex and precise multigenic devices, including genome editing tools. The starting point of this Thesis was the modular DNA assembly method known as GoldenBraid (GB), based on type IIS restriction enzymes. To further optimize the GB construct-making process and to better catalog the phytobricks collection, a database and a set of software-tools were developed as described in Chapter 1. The final webbased software package, released as GB2.0, was made publicly available at www.gbcloning.upv.es. A detailed description of the functioning of GB2.0, exemplified with the building of a multigene construct for anthocyanin overproduction was also provided in Chapter 1. As the number and complexity of GB constructs increased, the next step forward consisted in the refinement of the standards with the incorporation of experimental information associated to each genetic element (described in Chapter 2). To this end, the GB package was reshaped into an improved version (GB3.0), which is a self-contained, fully traceable assembly system where the experimental data describing the functionality of each DNA element is displayed in the form of a standard datasheet. The utility of the technical specifications to anticipate the behavior of composite devices was exemplified with the combination of a chemical switch with a prototype of an anthocyanin overproduction module equivalent to the one described in Chapter 1, resulting in a dexamethasone-responsive anthocyanin device. Furthermore, Chapter 3 describes the adaptation and functional characterization of CRISPR/Cas9 genome engineering tools to the GB technology. The performance of the adapted tools for gene editing, transcriptional activation and repression was successfully validated by transient expression in N. benthamiana. Finally, Chapter 4 presents a practical implementation of GB technology for precision plant breeding. An intragenic construct comprising an intragenic selectable marker and a master regulator of the flavonoid biosynthesis was stably transformed in tomato resulting in fruits enhanced in flavonol content. All together, this Thesis shows the implementation of increasingly complex and precise genetic designs in plants using standard elements and modular tools following the principles of Synthetic Biology.
[ES] La Biología Sintética es un campo emergente de carácter interdisciplinar que se fundamenta en la aplicación de los principios ingenieriles de modularidad, abstracción y estandarización a la ingeniería genética. Una nueva vertiente de la Biología Sintética aplicada a las plantas, la Biología Sintética Vegetal (BSV), ofrece nuevas posibilidades de mejora de cultivos que podrían llevar a una mejora de la resistencia, a una mayor productividad, o a un aumento de la calidad nutricional. Sin embargo, para alcanzar este fin las herramientas moleculares disponibles en estos momentos para BSV deben ser adaptadas para convertirse en modulares, estándares y más precisas. Por ello se planteó como objetivo general de esta Tesis adaptar, expandir y refinar las herramientas de ensamblaje de DNA de la BSV para permitir la incorporación de especificaciones funcionales en la descripción de elementos genéticos estándar (fitobricks) y facilitar la construcción de estructuras multigénicas cada vez más complejas y precisas, incluyendo herramientas de editado genético. El punto de partida de esta Tesis fue el método de ensamblaje modular de ADN GoldenBraid (GB) basado en enzimas de restricción tipo IIS. Para optimizar el proceso de ensamblaje y catalogar la colección de fitobricks generados se desarrollaron una base de datos y un conjunto de herramientas software, tal y como se describe en el Capítulo 1. El paquete final de software se presentó en formato web como GB2.0, haciéndolo accesible al público a través de www.gbcloning.upv.es. El Capítulo 1 también proporciona una descripción detallada del funcionamiento de GB2.0 ejemplificando su uso con el ensamblaje de una construcción multigénica para la producción de antocianinas. Con el aumento en número y complejidad de las construcciones GB, el siguiente paso necesario fue el refinamiento de los estándar con la incorporación de la información experimental asociada a cada elemento genético (se describe en el Capítulo 2). Para este fin, el paquete de software de GB se reformuló en una nueva versión (GB3.0), un sistema de ensamblaje auto-contenido y completamente trazable en el que los datos experimentales que describen la funcionalidad de cada elemento genético se muestran en forma de una hoja de datos estándar. La utilidad de las especificaciones técnicas para anticipar el comportamiento de dispositivos biológicos compuestos se ejemplificó con la combinación de un interruptor químico y un prototipo de un módulo de sobreproducción de antocianinas equivalente al descrito en el Capítulo 1, resultando en un dispositivo de producción de antocianinas con respuesta a dexametasona. Además, en el Capítulo 3 se describe la adaptación a la tecnología GB de las herramientas de ingeniería genética CRISPR/Cas9, así como su caracterización funcional. La funcionalidad de estas herramientas para editado génico y activación y represión transcripcional se validó con el sistema de expresión transitoria en N.benthamiana. Finalmente, el Capítulo 4 presenta una implementación práctica del uso de la tecnología GB para hacer mejora vegetal de manera precisa. La transformación estable en tomate de una construcción intragénica que comprendía un marcador de selección intragénico y un regulador de la biosíntesis de flavonoides resultó en frutos con un mayor contenido de flavonoles. En conjunto, esta Tesis muestra la implementación de diseños genéticos cada vez más complejos y precisos en plantas utilizando elementos estándar y herramientas modulares siguiendo los principios de la Biología Sintética.
[CAT] La Biologia Sintètica és un camp emergent de caràcter interdisciplinar que es fonamenta amb l'aplicació a la enginyeria genètica dels principis de modularitat, abstracció i estandarització. Una nova vessant de la Biologia Sintètica aplicada a les plantes, la Biologia Sintètica Vegetal (BSV), ofereix noves possibilitats de millora de cultius que podrien portar a una millora de la resistència, a una major productivitat, o a un augment de la qualitat nutricional. Tanmateix, per poder arribar a este fi les eines moleculars disponibles en estos moments per a la BSV han d'adaptar-se per convertir-se en modulars, estàndards i més precises. Per això es plantejà com objectiu general d'aquesta Tesi adaptar, expandir i refinar les eines d'ensamblatge d'ADN de la BSV per permetre la incorporació d'especificacions funcionals en la descripció d'elements genètics estàndards (fitobricks) i facilitar la construcció d'estructures multigèniques cada vegada més complexes i precises, incloent eines d'edidat genètic. El punt de partida d'aquesta Tesi fou el mètode d'ensamblatge d'ADN modular GoldenBraid (GB) basat en enzims de restricció tipo IIS. Per optimitzar el proces d'ensamblatge i catalogar la col.lecció de fitobricks generats es desenvolupà una base de dades i un conjunt d'eines software, tal i com es descriu al Capítol 1. El paquet final de software es presentà en format web com GB2.0, fent-se accessible al públic mitjançant la pàgina web www.gbcloning.upv.es. El Capítol 1 també proporciona una descripció detallada del funcionament de GB2.0, exemplificant el seu ús amb l'ensamblatge d'una construcció multigènica per a la producció d'antocians. Amb l'augment en nombre i complexitat de les construccions GB, el següent pas fou el refinament dels estàndards amb la incorporació de la informació experimental associada a cada element genètic (es descriu en el Capítol 2). Per a aquest fi, el paquet de software de GB es reformulà amb una nova versió anomenada GB3.0. Aquesta versió consisteix en un sistema d'ensamblatge auto-contingut i complemtament traçable on les dades experimentals que descriuen la funcionalitat de cada element genètic es mostren en forma de fulla de dades estàndard. La utilitat de les especificacions tècniques per anticipar el comportament de dispositius biològics compostos s'exemplificà amb la combinació de un interruptor químic i un prototip d'un mòdul de sobreproducció d'antocians equivalent al descrit al Capítol 1. Aquesta combinació va tindre com a resultat un dispositiu de producció d'antocians que respón a dexametasona. A més a més, al Capítol 3 es descriu l'adaptació a la tecnologia GB de les eines d'enginyeria genètica CRISPR/Cas9, així com la seua caracterització funcional. La funcionalitat d'aquestes eines per a l'editat gènic i activació i repressió transcripcional es validà amb el sistema d'expressió transitòria en N. benthamiana. Finalment, al Capítol 4 es presenta una implementació pràctica de l'ús de la tecnologia GB per fer millora vegetal de mode precís. La transformació estable en tomaca d'una construcció intragènica que comprén un marcador de selecció intragènic i un regulador de la biosíntesi de flavonoïdes resultà en plantes de tomaca amb un major contingut de flavonols en llur fruits. En conjunt, esta Tesi mostra la implementació de dissenys genètics cada vegada més complexos i precisos en plantes utilitzant elements estàndards i eines modulars seguint els principis de la Biologia Sintètica.
Vázquez Vilar, M. (2016). DESIGN OF GENETIC ELEMENTS AND SOFTWARE TOOLS FOR PLANT SYNTHETIC BIOLOGY [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/68483
TESIS
Premiado

Orientador: Mario Jino
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
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Resumo: Técnicas e critérios de teste de software estabelecem elementos requeridos a serem exercitados no teste. A geração de dados de teste visa selecionar dados de teste, do domínio multidimensional de entrada do software, para satisfazer um critério. Uma linha de trabalhos para a geração de dados de teste utiliza meta-heurísticas para buscar, no espaço de possíveis entradas do software, aquelas que satisfaçam um determinado critério, área referida como Teste de Software Baseado em Buscas. Esta tese propõe uma nova técnica, a Geração de Dados de Teste Orientada à Diversidade (Diversity Oriented Test Data Generation - DOTG). Esta técnica incorpora a intuição, encontrada em bons projetistas de teste, de que a variedade, ou diversidade, dos dados de teste tem um papel relevante para a completeza, ou qualidade, do teste realizado. São propostas diferentes perspectivas para a diversidade do teste; cada perspectiva leva em consideração um tipo de informação distinto para avaliar a diversidade. É definido também um meta-modelo para guiar o desenvolvimento das perspectivas da DOTG. É desenvolvida a perspectiva do domínio de entrada do software para a diversidade (DOTG-ID), que considera a posição dos dados de teste neste domínio para calcular a diversidade do conjunto de teste. São propostas uma medida de distância entre dados de teste e uma medida de diversidade de conjuntos de teste. São desenvolvidas três meta-heurísticas para a geração automática de dados de alta diversidade: a SA-DOTG, baseada em Recozimento Simulado; a GA-DOTG, baseada em Algoritmos Genéticos; e a SR-DOTG, baseada na dinâmica de sistemas de partículas eletricamente carregadas. A avaliação empírica da DOTG-ID inclui: uma simulação Monte Carlo, realizada com o objetivo de estudar a influência de fatores na eficácia da técnica; e um experimento com programas, realizado para avaliar o efeito da diversidade dos conjuntos de teste na cobertura alcançada, medida com respeito a critérios de teste baseados em análise de fluxos de dados e no critério baseado em defeitos Análise de Mutantes. Os resultados das avaliações, significativos estatisticamente, indicam que na maioria das situações os conjuntos de alta diversidade atingem eficácia e valores de cobertura maiores do que os alcançados pelos conjuntos gerados aleatoriamente, de mesmo tamanho
Abstract: Software testing techniques and criteria establish required elements to be exercised during testing. Test data generation aims at selecting test data from the multidimensional software's input domain to satisfy a given criterion. A set of works on test data generation apply metaheuristics to search in the space of possible inputs for the software for those inputs that satisfy a given criterion. This field is named Search Based Software Testing. This thesis proposes a new technique, the Diversity Oriented Test Data Generation - DOTG. This technique embodies the intuition, which can be found in good testers, that the variety, or diversity, of test data used to test a software has some relation with the completeness, or quality, of the testing performed. We propose different perspectives for the test diversity concept; each one takes into account a different kind of information to evaluate the diversity. A metamodel is also defined to guide de development of the DOTG perspectives. We developed the Input Domain perspective for diversity (DOTG-ID), which considers the positions of the test data in the software input domain to compute a diversity value for the test sets. We propose a measure of distance between test data and a measure of diversity of test sets. For the automatic generation of high diversity test sets three metaheuristics were developed: the SA-DOTG based on Simulated Annealing; the GADOTG based on Genetic Algorithms, and the SR-DOTG, based on the dynamics of particle systems electrically charged. The empirical evaluation of DOTG-ID includes: a Monte Carlo simulation performed to study the influence of factors on the technique's effectiveness, and an experiment with programs, carried out to evaluate the effect of the test sets diversity on the attained coverage values, measured with respect to data-flow coverage and to mutation coverage. The evaluation results statistically significant, pointing out that in most of cases the test sets with high diversity reach effectiveness and coverage values higher than the ones reached by randomly generated test sets of the same size
Doutorado
Engenharia de Computação
Doutor em Engenharia Elétrica

Maskininlärning har blivit allt vanligare inom näringslivet. Informationsinsamling med Data mining (DM) har expanderats och DM-utövare använder en mängd tumregler för att effektivisera tillvägagångssättet genom att undvika en anständig tid att ställa in hyperparametrarna för en given ML-algoritm för nå bästa träffsäkerhet. Förslaget i denna rapport är att införa ett tillvägagångssätt som systematiskt optimerar ML-algoritmerna med hjälp av genetiska algoritmer (GA), utvärderar om och hur modellen ska konstrueras för att hitta globala lösningar för en specifik datamängd. Genom att implementera genetiska algoritmer på två utvalda ML-algoritmer, K-nearest neighbors och Random forest, med två numeriska datamängder, Iris-datauppsättning och Wisconsin-bröstcancerdatamängd. Modellen utvärderas med träffsäkerhet och beräkningstid som sedan jämförs med sökmetoden exhaustive search. Resultatet har visat att GA fungerar bra för att hitta bra träffsäkerhetspoäng på en rimlig tid. Det finns vissa begränsningar eftersom parameterns betydelse varierar för olika ML-algoritmer.
As machine learning (ML) is being more and more frequent in the business world, information gathering through Data mining (DM) is on the rise, and DM-practitioners are generally using several thumb rules to avoid having to spend a decent amount of time to tune the hyperparameters (parameters that control the learning process) of an ML algorithm to gain a high accuracy score. The proposal in this report is to conduct an approach that systematically optimizes the ML algorithms using genetic algorithms (GA) and to evaluate if and how the model should be constructed to find global solutions for a specific data set. By implementing a GA approach on two ML-algorithms, K-nearest neighbors, and Random Forest, on two numerical data sets, Iris data set and Wisconsin breast cancer data set, the model is evaluated by its accuracy scores as well as the computational time which then is compared towards a search method, specifically exhaustive search. The results have shown that it is assumed that GA works well in finding great accuracy scores in a reasonable amount of time. There are some limitations as the parameter’s significance towards an ML algorithm may vary.

This master's thesis offers a brief introduction to evolutionary computation. It describes and compares the genetic programming and grammar based genetic programming and their potential use in automatic software repair. It studies possible applications of grammar based genetic programming on automatic software repair. Grammar based genetic programming is then used in design and implementation of a new method for automatic software repair. Experimental evaluation of the implemented automatic repair was performed on set of test programs.

In today’s technology driven society, software is becoming increasingly important in more areas of our lives. The domain of software extends beyond the obvious domain of computers, tablets, and mobile phones. Smart devices and the internet-of-things have inspired the integra- tion of digital and computational technology into objects that some of us would never have guessed could be possible or even necessary. Fridges and freezers connected to social media sites, a toaster activated with a mobile phone, physical buttons for shopping, and verbally asking smart speakers to order a meal to be delivered. This is the world we live in and it is an exciting time for software engineers and computer scientists. The sheer volume of code that is currently in use has long since outgrown beyond the point of any hope for proper manual maintenance. The rate of which mobile application stores such as Google’s and Apple’s have expanded is astounding. The research presented here aims to shed a light on an emerging field of research, called Genetic Improvement ( GI ) of software. It is a methodology to change program code to improve existing software. This thesis details a framework for GI that is then applied to explore fitness landscape of bug fixing Python software, reduce execution time in a C ++ program, and integrated into a live system. We show that software is generally not fragile and although fitness landscapes for GI are flat they are not impossible to search in. This conclusion applies equally to bug fixing in small programs as well as execution time improvements. The framework’s application is shown to be transportable between programming languages with minimal effort. Additionally, it can be easily integrated into a system that runs a live web service.

The main purpose of object-oriented programming is to use encapsulation to reduce the amount of coupling within each object. However, object-oriented programming has some weaknesses in this area. To address this shortcoming, researchers have proposed an approach known as aspect-oriented programming (AOP). AOP is intended to reduce the amount of tangled code within an application by grouping similar functions into an aspect. To demonstrate the powerful aspects of AOP, it is necessary to extract aspect candidates from current object-oriented applications. Many different approaches have been proposed to accomplish this task. One of such approaches utilizes vector based clustering to identify the possible aspect candidates. In this study, two different types of vectors are applied to two different vector-based clustering techniques. In this approach, each method in a software system S is represented by a d-dimensional vector. These vectors take into account the Fan-in values of the methods as well as the number of calls made to individual methods within the classes in software system S. Then a semi-supervised clustering approach known as Support Vector Clustering is applied to the vectors. In addition, an improved K-means clustering approach which is based on Genetic Algorithms is also applied to these vectors. The results obtained from these two approaches are then evaluated using standard metrics for aspect mining. In addition to introducing two new clustering based approaches to aspect mining, this research investigates the effectiveness of the currently known metrics used in aspect mining to evaluate a given vector based approach. Many of the metrics currently used for aspect mining evaluations are singleton metrics. Such metrics evaluate a given approach by taking into account only one aspect of a clustering technique. This study, introduces two different sets of metrics by combining these singleton measures. The iDIV metric combines the Diversity of a partition (DIV), Intra-cluster distance of a partition (IntraD), and the percentage of the number of methods analyzed (PAM) values to measure the overall effectiveness of the diversity of the partitions. While the iDISP metric combines the Dispersion of crosscutting concerns (DISP) along with Inter-cluster distance of a partition (InterD) and the PAM values to measure the quality of the clusters formed by a given method. Lastly, the oDIV and oDISP metrics introduced, take into account the complexity of the algorithms in relation with the DIV and DISP values. By comparing the obtained values for each of the approaches, this study is able to identify the best performing method as it pertains to these metrics.