Searching for invariants using genetic programming and mutation testing

Invariants are concise and useful descriptions of a program's behaviour. As most programs are not annotated with invariants, previous research has attempted to automatically generate them from source code. In this paper, we propose a new approach to invariant generation using search. We reuse the trace generation front-end of existing tool Daikon and integrate it with genetic programming and a mutation testing tool. We demonstrate that our system can find the same invariants through search that Daikon produces via template instantiation, and we also find useful invariants that Daikon does not. We then present a method of ranking invariants such that we can identify those that are most interesting, through a novel application of program mutation

Utilizing static and dynamic software analysis to aid cost estimation, software visualization, and test quality management

The main results presented in the thesis are related to the semi- or fully-automated analysis of the software and its development processes. My overall research goal is to provide meaningful insights, methods, and practical tools to help the work of stakeholders during various phases of software development. The thesis statements have been grouped into three major thesis points, namely "Measuring, predicting, and comparing the productivity of developer teams"; "Providing immersive methods for software and unit test visualization"; and "Spotting the structures in the package hierarchy that required attention using test coverage data"

Bytecode-based Multiple Condition Coverage: An Initial Investigation

Masking occurs when one condition prevents another condition from influencing the output of a Boolean expression. Logic-based adequacy criteria such as Multiple Condition Coverage (MCC) are designed to overcome masking at the within-expression level, but can offer no guarantees about masking in subsequent expressions. As a result, a Boolean expression written as a single complex statement will yield test cases that are more likely to overcome masking than when the expression is written as series of simple statements. Many approaches to automated analysis and test case generation for Java systems operate not on the source code representation of code, but on the bytecode. The transformation from source code to bytecode requires simplifying code elements, introducing the risk of masking. We propose Bytecode-MCC, designed to group related Boolean expressions from the bytecode, reformulate the expressions into a single complex expression, and produce test cases satisfying each combination of conditions in the constructed expression. Bytecode-MCC should produce test obligations that—when satisfied—are more likely to reveal faults in the program logic than tests providing coverage of existing criteria over the simplified bytecode. A preliminary study has hinted at the potential of this approach. However, Bytecode-MCC is more difficult to achieve than Branch Coverage, and means of increasing coverage are needed to truly test the fault-detection potential of this technique. We propose methods of improving Bytecode-MCC coverage through automated generation that we will explore in future work

Prospect patents, data markets, and the commons in data-driven medicine : openness and the political economy of intellectual property rights

Scholars who point to political influences and the regulatory function of patent courts in the USA have long questioned the courts’ subjective interpretation of what ‘things’ can be claimed as inventions. The present article sheds light on a different but related facet: the role of the courts in regulating knowledge production. I argue that the recent cases decided by the US Supreme Court and the Federal Circuit, which made diagnostics and software very difficult to patent and which attracted criticism for a wealth of different reasons, are fine case studies of the current debate over the proper role of the state in regulating the marketplace and knowledge production in the emerging information economy. The article explains that these patents are prospect patents that may be used by a monopolist to collect data that everybody else needs in order to compete effectively. As such, they raise familiar concerns about failure of coordination emerging as a result of a monopolist controlling a resource such as datasets that others need and cannot replicate. In effect, the courts regulated the market, primarily focusing on ensuring the free flow of data in the emerging marketplace very much in the spirit of the ‘free the data’ language in various policy initiatives, yet at the same time with an eye to boost downstream innovation. In doing so, these decisions essentially endorse practices of personal information processing which constitute a new type of public domain: a source of raw materials which are there for the taking and which have become most important inputs to commercial activity. From this vantage point of view, the legal interpretation of the private and the shared legitimizes a model of data extraction from individuals, the raw material of information capitalism, that will fuel the next generation of data-intensive therapeutics in the field of data-driven medicine

Automated specification-based testing of graphical user interfaces

Tese de doutoramento. Engenharia Electrónica e de Computadores. 2006. Faculdade de Engenharia. Universidade do Porto, Departamento de Informática, Escola de Engenharia. Universidade do Minh

Camera simulation for YoloV5 training and optimization

openOggigiorno, una delle sfide più grandi nell’ambito del machine learning e dell’intelligenza artificiale è l’acquisizione di dati etichettati, in quanto è usualmente un compito molto impattante in termini di tempo e costi. L’obiettivo di questo elaborato è quello di proporre una soluzione che cerca di automatizzare questo processo adattando un già esistente software di rendering di simulazione robotica per la creazione di immagini sintetiche. Queste immagini verranno poi utilizzate per allenare l’algoritmo YoloV5 e sarà verificata la qualità delle prestazioni su nuove immagini reali.Nowadays, one of the biggest challenges in machine learning and artificial intelligence is the acquisition of labeled data since it usually is a heavy time and money consuming task. The aim of this document is to propose a solution that tries to automatize this process by adapting an already existing robot simulator’s rendering software in order to produce synthetic images. Those images will then be used to train the YoloV5 algorithm and it will be verified how well the algorithm performs onto real new images