Content-Based Textual File Type Detection at Scale

Programming language detection is a common need in the analysis of large source code bases. It is supported by a number of existing tools that rely on several features, and most notably file extensions, to determine file types. We consider the problem of accurately detecting the type of files commonly found in software code bases, based solely on textual file content. Doing so is helpful to classify source code that lack file extensions (e.g., code snippets posted on the Web or executable scripts), to avoid misclassifying source code that has been recorded with wrong or uncommon file extensions, and also shed some light on the intrinsic recognizability of source code files. We propose a simple model that (a) use a language-agnostic word tokenizer for textual files, (b) group tokens in 1-/2-grams, (c) build feature vectors based on N-gram frequencies, and (d) use a simple fully connected neural network as classifier. As training set we use textual files extracted from GitHub repositories with at least 1000 stars, using existing file extensions as ground truth. Despite its simplicity the proposed model reaches 85% in our experiments for a relatively high number of recognized classes (more than 130 file types)

The Derivation of a pragmatic requirements framework for web development.

Web-based development is a relatively immature area of Software Engineering, producing often complex applications to many different types of end user and stakeholders. Web Engineering as a research area, was created to introduce processes that enable web based development to be repeatable and to avoid potential failure in the fast changing landscape that is the current ubiquitous Internet. A survey of existing perspectives from the literature highlights a number of points. Firstly, that web development has a number of subtle differences to Software Engineering and that many web development methods are not used. Further, that there has been little work done on what should be in a web development method. A full survey of 50 web development methods finds that they do not give enough detail to be used in their entirety; they are difficult for a non-computer scientist to understand in the techniques they use and most do not cover the lifecycle, particularly in the area of requirements, implementation and testing. This thesis introduces a requirements framework for novice web developers. It is created following an in-depth case study carried out over two years that investigates the use of web development methods by novice developers. The study finds that web development methods are not easy to understand, there is a lack of explanation as to how to use the techniques within the method and the language used is too complex. A high level method is derived with an iterative process and with the requirements phase in the form of a framework; it addresses the problems that are discussed and provides excellent support for a novice web developer in the requirements phase of the lifecycle. An evaluation of the method using a group of novice developers who reflect on the method and a group who use it for development finds that the method is both easy to understand and use

A COMPARISON BETWEEN MOTIVATIONS AND PERSONALITY TRAITS IN RELIGIOUS TOURISTS AND CRUISE SHIP TOURISTS

The purpose of this paper is to analyze the motivations and the personality traits that characterize tourists who choose religious travels versus cruises. Participating in the research were 683 Italian tourists (345 males and 338 females, age range 18–63 years); 483 who went to a pilgrimage travel and 200 who chose a cruise ship in the Mediterranean Sea. Both groups of tourists completed the Travel Motivation Scale and the Big Five Questionnaire. Results show that different motivations and personality traits characterize the different types of tourists and, further, that motivations for traveling are predicted by specific —some similar, other divergent— personality trait

The derivation of a pragmatic requirements framework for web development

Web-based development is a relatively immature area of Software Engineering, producing often complex applications to many different types of end user and stakeholders. Web Engineering as a research area, was created to introduce processes that enable web based development to be repeatable and to avoid potential failure in the fast changing landscape that is the current ubiquitous Internet. A survey of existing perspectives from the literature highlights a number of points. Firstly, that web development has a number of subtle differences to Software Engineering and that many web development methods are not used. Further, that there has been little work done on what should be in a web development method. A full survey of 50 web development methods finds that they do not give enough detail to be used in their entirety; they are difficult for a non-computer scientist to understand in the techniques they use and most do not cover the lifecycle, particularly in the area of requirements, implementation and testing. This thesis introduces a requirements framework for novice web developers. It is created following an in-depth case study carried out over two years that investigates the use of web development methods by novice developers. The study finds that web development methods are not easy to understand, there is a lack of explanation as to how to use the techniques within the method and the language used is too complex. A high level method is derived with an iterative process and with the requirements phase in the form of a framework; it addresses the problems that are discussed and provides excellent support for a novice web developer in the requirements phase of the lifecycle. An evaluation of the method using a group of novice developers who reflect on the method and a group who use it for development finds that the method is both easy to understand and use.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Big Code Applications and Approaches

The availability of a huge amount of source code from code archives and open-source projects opens up the possibility to merge machine learning, programming languages, and software engineering research fields. This area is often referred to as Big Code where programming languages are treated instead of natural languages while different features and patterns of code can be exploited to perform many useful tasks and build supportive tools. Among all the possible applications which can be developed within the area of Big Code, the work presented in this research thesis mainly focuses on two particular tasks: the Programming Language Identification (PLI) and the Software Defect Prediction (SDP) for source codes. Programming language identification is commonly needed in program comprehension and it is usually performed directly by developers. However, when it comes at big scales, such as in widely used archives (GitHub, Software Heritage), automation of this task is desirable. To accomplish this aim, the problem is analyzed from different points of view (text and image-based learning approaches) and different models are created paying particular attention to their scalability. Software defect prediction is a fundamental step in software development for improving quality and assuring the reliability of software products. In the past, defects were searched by manual inspection or using automatic static and dynamic analyzers. Now, the automation of this task can be tackled using learning approaches that can speed up and improve related procedures. Here, two models have been built and analyzed to detect some of the commonest bugs and errors at different code granularity levels (file and method levels). Exploited data and models’ architectures are analyzed and described in detail. Quantitative and qualitative results are reported for both PLI and SDP tasks while differences and similarities concerning other related works are discussed