Using Word Embedding and Convolution Neural Network for Bug Triaging by Considering Design Flaws

Resolving bugs in the maintenance phase of software is a complicated task. Bug assignment is one of the main tasks for resolving bugs. Some Bugs cannot be fixed properly without making design decisions and have to be assigned to designers, rather than programmers, to avoid emerging bad smells that may cause subsequent bug reports. Hence, it is important to refer some bugs to the designer to check the possible design flaws. Based on our best knowledge, there are a few works that have considered referring bugs to designers. Hence, this issue is considered in this work. In this paper, a dataset is created, and a CNN-based model is proposed to predict the need for assigning a bug to a designer by learning the peculiarities of bug reports effective in creating bad smells in the code. The features of each bug are extracted from CNN based on its textual features, such as a summary and description. The number of bad samples added to it in the fixing process using the PMD tool determines the bug tag. The summary and description of the new bug are given to the model and the model predicts the need to refer to the designer. The accuracy of 75% (or more) was achieved for datasets with a sufficient number of samples for deep learning-based model training. A model is proposed to predict bug referrals to the designer. The efficiency of the model in predicting referrals to the designer at the time of receiving the bug report was demonstrated by testing the model on 10 projects

A Survey of Learning-based Automated Program Repair

Automated program repair (APR) aims to fix software bugs automatically and plays a crucial role in software development and maintenance. With the recent advances in deep learning (DL), an increasing number of APR techniques have been proposed to leverage neural networks to learn bug-fixing patterns from massive open-source code repositories. Such learning-based techniques usually treat APR as a neural machine translation (NMT) task, where buggy code snippets (i.e., source language) are translated into fixed code snippets (i.e., target language) automatically. Benefiting from the powerful capability of DL to learn hidden relationships from previous bug-fixing datasets, learning-based APR techniques have achieved remarkable performance. In this paper, we provide a systematic survey to summarize the current state-of-the-art research in the learning-based APR community. We illustrate the general workflow of learning-based APR techniques and detail the crucial components, including fault localization, patch generation, patch ranking, patch validation, and patch correctness phases. We then discuss the widely-adopted datasets and evaluation metrics and outline existing empirical studies. We discuss several critical aspects of learning-based APR techniques, such as repair domains, industrial deployment, and the open science issue. We highlight several practical guidelines on applying DL techniques for future APR studies, such as exploring explainable patch generation and utilizing code features. Overall, our paper can help researchers gain a comprehensive understanding about the achievements of the existing learning-based APR techniques and promote the practical application of these techniques. Our artifacts are publicly available at \url{https://github.com/QuanjunZhang/AwesomeLearningAPR}

SIT automation tool: failure use case automation and diagnosis

Study of systems to manage the performance and quality of service of wireless data networks. Work with optimization techniques and project management to solve complex networks issues.The scope of this thesis is the SIT (System Integration Testing) process which is the testing procedure executed in customer test environment before the software goes on production environment. The main objective for this thesis is no other than improving the current process step by step taking into account the automation, efficiency, missing checks and much more. This project is a kind of Industrial process to create a powerful testing tool which can allow the company to deliver quality adaptor products efficiently, do better in less time helping to reduce costs, as Adaptors are the most demanded product of MYCOM OSI portfolio. Take into account that business is not only generated when an Adaptor is delivered for first time but also when Vendors provide with new releases and new functionalities and operators needs to order an upgrade of the Adaptor to be able to monitor the new functionalities deployed on their network.El campo de aplicación en el que está centrado esta tesis es el SIT (System Integration Testing), proceso de testeo ejecutado en un servidor de testeo del cliente antes de desplegar el software el medio de producción. El objetivo principal de esta tesis no es otro que mejorar el proceso actual paso a paso teniendo en cuenta la automatización, eficiencia, la falta de verificaciones, entre otros. Este proyecto es una especie de proceso industrial para crear una aplicación potente de testeo que pueda permitir a la compañía entregar adaptadores de calidad con eficiencia, que hagan más en menos tiempo ayudando así a reducir costes. Los adaptadores son el producto más demandado del porfolio de MYCOM OSI. Hay que tener en cuenta que el negocio no se genera solamente cuando se entrega por primera vez el adaptador al cliente, sino que cuando los proveedores lanzan nuevas versiones con nuevas funcionalidades y los operadores necesitan encargar una mejora del adaptador para poder monitorizar las nuevas funcionalidades desplegadas en su red.El camp d'aplicació en que es basa aquesta tesi és el SIT (System Integration Testing), procés de testeig executat en un servidor de testeig del client abans de desplegar el software al mitjà de producció. L'objectiu principal d'aquesta tesi no és un altre que millorar el procés actual pas a pas tenint en compte l'automatització, l'eficiència, la falta de verificacions, d'entre altres. Aquest projecte és una mena de procés industrial per crear una aplicació potent de testeig que pugui permetre a la companyia lliurar adaptadors de qualitat amb eficiència, que facin més en menys temps ajudant així a reduir costos. Els adaptadors són el producte més demandat del porfolio de MYCOM OSI. Cal tenir en compte que el negoci no només es genera quan es lliura per primera vegada l'adaptador al client, sinó que quan els proveïdors llancen noves versions amb noves funcionalitats i els operadors necessiten encarregar una millora de l'adaptador per poder monitoritzar les noves funcionalitats desplegades a la seva xarxa

Large Language Models for Software Engineering: A Systematic Literature Review

Large Language Models (LLMs) have significantly impacted numerous domains, notably including Software Engineering (SE). Nevertheless, a well-rounded understanding of the application, effects, and possible limitations of LLMs within SE is still in its early stages. To bridge this gap, our systematic literature review takes a deep dive into the intersection of LLMs and SE, with a particular focus on understanding how LLMs can be exploited in SE to optimize processes and outcomes. Through a comprehensive review approach, we collect and analyze a total of 229 research papers from 2017 to 2023 to answer four key research questions (RQs). In RQ1, we categorize and provide a comparative analysis of different LLMs that have been employed in SE tasks, laying out their distinctive features and uses. For RQ2, we detail the methods involved in data collection, preprocessing, and application in this realm, shedding light on the critical role of robust, well-curated datasets for successful LLM implementation. RQ3 allows us to examine the specific SE tasks where LLMs have shown remarkable success, illuminating their practical contributions to the field. Finally, RQ4 investigates the strategies employed to optimize and evaluate the performance of LLMs in SE, as well as the common techniques related to prompt optimization. Armed with insights drawn from addressing the aforementioned RQs, we sketch a picture of the current state-of-the-art, pinpointing trends, identifying gaps in existing research, and flagging promising areas for future study