Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration

Testing in Continuous Integration (CI) involves test case prioritization, selection, and execution at each cycle. Selecting the most promising test cases to detect bugs is hard if there are uncertainties on the impact of committed code changes or, if traceability links between code and tests are not available. This paper introduces Retecs, a new method for automatically learning test case selection and prioritization in CI with the goal to minimize the round-trip time between code commits and developer feedback on failed test cases. The Retecs method uses reinforcement learning to select and prioritize test cases according to their duration, previous last execution and failure history. In a constantly changing environment, where new test cases are created and obsolete test cases are deleted, the Retecs method learns to prioritize error-prone test cases higher under guidance of a reward function and by observing previous CI cycles. By applying Retecs on data extracted from three industrial case studies, we show for the first time that reinforcement learning enables fruitful automatic adaptive test case selection and prioritization in CI and regression testing.Comment: Spieker, H., Gotlieb, A., Marijan, D., & Mossige, M. (2017). Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration. In Proceedings of 26th International Symposium on Software Testing and Analysis (ISSTA'17) (pp. 12--22). AC

Extending Nunchaku to Dependent Type Theory

Nunchaku is a new higher-order counterexample generator based on a sequence of transformations from polymorphic higher-order logic to first-order logic. Unlike its predecessor Nitpick for Isabelle, it is designed as a stand-alone tool, with frontends for various proof assistants. In this short paper, we present some ideas to extend Nunchaku with partial support for dependent types and type classes, to make frontends for Coq and other systems based on dependent type theory more useful.Comment: In Proceedings HaTT 2016, arXiv:1606.0542

Relay: A New IR for Machine Learning Frameworks

Machine learning powers diverse services in industry including search, translation, recommendation systems, and security. The scale and importance of these models require that they be efficient, expressive, and portable across an array of heterogeneous hardware devices. These constraints are often at odds; in order to better accommodate them we propose a new high-level intermediate representation (IR) called Relay. Relay is being designed as a purely-functional, statically-typed language with the goal of balancing efficient compilation, expressiveness, and portability. We discuss the goals of Relay and highlight its important design constraints. Our prototype is part of the open source NNVM compiler framework, which powers Amazon's deep learning framework MxNet

Segmentation of articular cartilage and early osteoarthritis based on the fuzzy soft thresholding approach driven by modified evolutionary ABC optimization and local statistical aggregation

Articular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel's classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel's membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise.Web of Science117art. no. 86

Auotmatic detection, consistent mapping, and training

Results from two experiments showed that a flat display﷓size function was found under the consistent mapping (CM) condition despite the facts that there was no extensive CM training and that the stimulus﷓response (S﷓R) consistency was only an intrasession manipulation. A confounding factor might be responsible for the fact that the consistent and the varied S﷓R mapping conditions gave rise to different display﷓size functions in Schneider and Shiffrin's (1977) study. Their claim that automatic detection and controlled search are qualitatively different is also discussed

Study of automatic differentiation in topology optimization

This bachelor final thesis presents a study on the integration of automatic differentiation functions into basic topology optimisation algorithms to improve not only computation speed, but also efficiency and accuracy. The main goal is to develop a fully functional automatic differentiation script capable of deriving topological expression, linear or non linear ones, aiming to find the optimal distribution. Moreover, the objectives of this research are to explore the application of automatic differentiation in fields related to topology optimisation, analyse the benefits of applying this methods and its disadvantages and review its computational efficiency. The thesis begins by introducing the fundamentals of automatic differentiation. Beforehand, during the initial stages of the project extensive practice of Matlab programming and object oriented programming was taught but it is not included in this report. A literature review is conducted to examine existing studies and approaches that utilize AD techniques. Furthermore, we dig into different AD methods, including forward mode and reverse mode, highlighting its approach with Matlab language and their advantages and limitations. Additionally, specific topologic optimisation tools and software packages commonly used are reviewed but are not included in this report. The report continues by presenting the different discretisation cases in finite element method and developing an AD based case to solve the discretisation of different 2 dimension problems. Deriving its shape functions and testing AD for a future, more sophisticated, topological problem. To achieve the main objective of performing, at least, one topology case using automatic differentiation, an AD based algorithm using different iterative methods is developed and implemented. The algorithm is tested with basic shapes and problems to be improved and more efficient, including all the possible casuistry of a mathematical expression. Ending with the research, we test different topological cases using different iterative methods. One of these methods, newton iteration method, will need an improvement to higher order gradients of the automatic differentiation algorithm. With this improvement we will test and compare both methods for several cases to conclude about the efficiency, accuracy and computing time of both iterative methods and automatic differentiation algorithm applied to topological problems. The results of the study demonstrate that automatic differentiation significantly enhances the efficiency and accuracy of topological optimisation for a certain type of problems. For these cases, AD exhibits faster convergence, improved accuracy in gradient computation, and reduced computational time. Moreover, the AD-based approach proves to be robust and applicable to not only deriving structural function, but also different problem domains, highlighting its versatility and practicality. Overall, The research highlights the potential of AD in many fields

Applying Formal Methods to Networking: Theory, Techniques and Applications

Despite its great importance, modern network infrastructure is remarkable for the lack of rigor in its engineering. The Internet which began as a research experiment was never designed to handle the users and applications it hosts today. The lack of formalization of the Internet architecture meant limited abstractions and modularity, especially for the control and management planes, thus requiring for every new need a new protocol built from scratch. This led to an unwieldy ossified Internet architecture resistant to any attempts at formal verification, and an Internet culture where expediency and pragmatism are favored over formal correctness. Fortunately, recent work in the space of clean slate Internet design---especially, the software defined networking (SDN) paradigm---offers the Internet community another chance to develop the right kind of architecture and abstractions. This has also led to a great resurgence in interest of applying formal methods to specification, verification, and synthesis of networking protocols and applications. In this paper, we present a self-contained tutorial of the formidable amount of work that has been done in formal methods, and present a survey of its applications to networking.Comment: 30 pages, submitted to IEEE Communications Surveys and Tutorial