Classification of machine learning engines using latent semantic indexing

With the huge increase of software functionalities, sizes and application domain, the difficulty of categorizing and classifying software for information retrieval and maintenance purposes is on demand.This work includes the use of Latent Semantic Indexing (LSI) in classifying neural network and k-nearest neighborhood source code programs. Functional descriptors of each program are identified by extracting terms contained in the source code.In addition, information on where the terms are extracted from is also incorporated in the LSI.Based on the undertaken experiment, the LSI classifier is noted to generate a higher precision and recall compared to the C4.5 algorithm as provided in the Weka tool

Mega Software Engineering

Techinical Report of Software Engineering Lab in Osaka Univ. SEL-Sep-22-200

Automatic multi-label categorization of Java applications using Dependency graphs

Automatic approaches for categorization of software repositories are increasingly gaining acceptance because they reduce manual effort and can produce high quality results. Most of the existing approaches have strongly relied on supervised machine learning {which requires a set of predefined categories to be used as training data{ and have used source code, comments, API Calls and other sources to obtain information about the projects to be categorized. We consider that existing approaches have weaknesses that can have major implications on the categorization results and haven't been solved at the same time, namely the assumption of non-restricted access to source code and the use of predefined sets of categories. Therefore, we present Sally: a novel, unsupervised and multi-label automatic categorization model that is able to obtain meaningful categories without depending on access to source code nor the existence of predefined categories by leveraging on information obtained from the projects in the categorization corpus and the dependency relations between them. We performed two experiments in which we compared Sally to the categorization strategies of two widely used websites and to MUDABlue, a categorization model proposed by Kawaguchi et al. that we consider to be a good baseline. Additionally, we assessed the proposed model by conducting a survey with 14 developers with a wide range of programming experience and developed a web application to make the proposed model available to potential users.Resumen. La categorización automática de repositorios de software ha ido ganando aceptación debido a que reduce el esfuerzo manual y puede generar resultados de alta calidad. La mayoría de los modelos existentes dependen fuertemente del aprendizaje de máquina supervisado { que necesita de un conjunto predefinido de categorías para ser usado como datos de entrenamiento{ y han usado código fuente, comentarios, llamadas de API y otras fuentes para obtener información sobre los proyectos a categorizar. Consideramos que los modelos existentes tienen debilidades que pueden tener implicaciones importantes en el resultado de la categorización y no han sido resueltas al mismo tiempo, específicamente la suposición de que el código fuente de los proyectos se encuentra completamente disponible y la necesidad de conjuntos predefinidos de categorías. Por esto, presentamos el modelo Sally: Un enfoque de categorización automática de software novedoso, no supervisado y multi-etiqueta que es capaz de generar categorías descriptivas sin depender del acceso al código fuente ni a categorías predefinidas usando información obtenida de los proyectos a categorizar y las relaciones entre ellos. Realizamos dos experimentos en los que comparamos a Sally con las estrategias de categorización automática de dos herramientas online ámpliamente utilizadas y con MUDABlue, un modelo de categorización automática de software propuesto por Kawaguchi et al. que consideramos una buena base de comparación. Adicionalmente, evaluamos el modelo propuesto por medio de un caso de estudio llevado a cabo con la participación de 14 desarrolladores con un ámplio rango de experiencia en programación y desarrollamos una aplicación web para poner el modelo propuesto a disposición de usuarios potenciales.Maestrí

A model for automatic categorization of software applications using non-parametric clustering and bytecode analysis

Automatic software categorization is the task of assigning software systems or libraries to categories based on their functionality. Correctly assigning these categories is essential to ensure that relevant libraries can be easily retrieved by developers from large repositories. State of the art approaches rely on the semantics reflected by identifiers and comments in the source code of the libraries in order to determine their category. However, these approaches fail when the source code of the libraries is not available. In this document, we describe a novel approach for the automatic categorization of Java libraries, which needs only the bytecode of a library in order to determine its category. We show that the approach, based on Dirichlet Process Clustering with automatic labeling, is able to successfully categorize libraries from the Apache Foundation Repository.Resumen. Categorización automática de software es la tarea de asignar categorias o etiquetas a aplicaciones o librerias para representar su funcionalidad. Una asignación correcta de estas categorías es esencial para asegurar que las librerias puedan ser fácilmente consultadas y recuperadas por los desarolladores, cuando estos últimos usan grandes repositorios de software. Técnicas actuales se basan en la información semántica reflejada en los identificadores de código fuente y sus comentarios con el objetivo de determinar su categoría. Sin embargo, estas técnicas no son adecuadas cuando el código fuente de las aplicaciones o librerias no está disponible. En este documento, se describe una nueva técnica para la categorización automática de librerias escritas en Java, la cual necesita solo el bytecode de las librerias para asignarles una categoría. Este documento muestra que la técnica, basada en Dirichlet Process Clustering con etiquetado automático de clusters, es capaz de categorizar exitosamente librerias almacenadas en el repositorio del la Fundación Apache.Maestrí

EvoEvo Deliverable 6.8: Final report

Final report of the EvoEvo project

Reservoir Computing in Materio

Reservoir Computing first emerged as an efficient mechanism for training recurrent neural networks and later evolved into a general theoretical model for dynamical systems. By applying only a simple training mechanism many physical systems have become exploitable unconventional computers. However, at present, many of these systems require careful selection and tuning by hand to produce usable or optimal reservoir computers. In this thesis we show the first steps to applying the reservoir model as a simple computational layer to extract exploitable information from complex material substrates. We argue that many physical substrates, even systems that in their natural state might not form usable or "good" reservoirs, can be configured into working reservoirs given some stimulation. To achieve this we apply techniques from evolution in materio whereby configuration is through evolved input-output signal mappings and targeted stimuli. In preliminary experiments the combined model and configuration method is applied to carbon nanotube/polymer composites. The results show substrates can be configured and trained as reservoir computers of varying quality. It is shown that applying the reservoir model adds greater functionality and programmability to physical substrates, without sacrificing performance. Next, the weaknesses of the technique are addressed, with the creation of new high input-output hardware system and an alternative multi-substrate framework. Lastly, a substantial effort is put into characterising the quality of a substrate for reservoir computing, i.e its ability to realise many reservoirs. From this, a methodological framework is devised. Using the framework, radically different computing substrates are compared and assessed, something previously not possible. As a result, a new understanding of the relationships between substrate, tasks and properties is possible, outlining the way for future exploration and optimisation of new computing substrates

Software Engineering in the Age of App Stores: Feature-Based Analyses to Guide Mobile Software Engineers

Mobile app stores are becoming the dominating distribution platform of mobile applications. Due to their rapid growth, their impact on software engineering practices is not yet well understood. There has been no comprehensive study that explores the mobile app store ecosystem's effect on software engineering practices. Therefore, this thesis, as its first contribution, empirically studies the app store as a phenomenon from the developers' perspective to investigate the extent to which app stores affect software engineering tasks. The study highlights the importance of a mobile application's features as a deliverable unit from developers to users. The study uncovers the involvement of app stores in eliciting requirements, perfective maintenance and domain analysis in the form of discoverable features written in text form in descriptions and user reviews. Developers discover possible features to include by searching the app store. Developers, through interviews, revealed the cost of such tasks given a highly prolific user base, which major app stores exhibit. Therefore, the thesis, in its second contribution, uses techniques to extract features from unstructured natural language artefacts. This is motivated by the indication that developers monitor similar applications, in terms of provided features, to understand user expectations in a certain application domain. This thesis then devises a semantic-aware technique of mobile application representation using textual functionality descriptions. This representation is then shown to successfully cluster mobile applications to uncover a finer-grained and functionality-based grouping of mobile apps. The thesis, furthermore, provides a comparison of baseline techniques of feature extraction from textual artefacts based on three main criteria: silhouette width measure, human judgement and execution time. Finally, this thesis, in its final contribution shows that features do indeed migrate in the app store beyond category boundaries and discovers a set of migratory characteristics and their relationship to price, rating and popularity in the app stores studied