A learning algorithm for optimizing continuous integration development and testing practice

Continuous integration, at its core, includes a set of practices that aim to prevent and reduce the cost of software integration issues by merging working software copies often. Regression testing is considered a good practice in software development with continuous integration, which ensures that code changes are not negatively affecting software functionality. As, nowadays, software development is carried out iteratively, with small code increments continuously developed and regression tested, it is of critical importance that continuous regression testing is time efficient. However, in practice, regression testing is often long lasting and faces scalability problems as software grows larger or as software changes are made more frequently. One contributing factor to these issues is test redundancy, which causes the same software functionality being tested multiple times across a test suite. In large-scale software, especially highly configurable software, redundancy in continuous regression testing can significantly grow the size of test suites and negatively affect the cost effectiveness of continuous integration. This paper presents a practical learning algorithm for optimizing continuous integration testing by reducing ineffective test redundancy in regression suites. The novelty of the algorithm lies in learning and predicting the fault-detection effectiveness of continuous integration tests using historical test records and combining this information with coverage-based redundancy metrics. The goal is to identify ineffective redundancy, which is maximally reduced in the resulting regression test suite, thus reducing test time and improving the performance of continuous integration. We apply and evaluate the algorithm in two industrial projects of continuous integration. The results show that the proposed algorithm can improve the efficiency of continuous integration practice in terms of decreasing test execution time by 38% on average compared to the industry practice of our case study and by 40% on average compared to the retest-all approach. The results further demonstrate no significant reduction in fault-detection effectiveness of continuous regression testing. This suggests that the proposed algorithm contributes to the state of the practice in the continuous integration development and testing of highly configurable systems.     

Priority-based scheduling of mixed-critical jobs

Real-Time Systems - Modern real-time systems tend to be mixed-critical, in the sense that they integrate on the same computational platform applications at different levels of criticality...     

Sparse probit linear mixed model

Linear mixed models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a continuous phenotype of interest, while simultaneously correcting for various confounding factors such as age, ethnicity and population structure. Formulated as models for linear regression, LMMs have been restricted to continuous phenotypes. We introduce the sparse probit linear mixed model (Probit-LMM), where we generalize the LMM modeling paradigm to binary phenotypes. As a technical challenge, the model no longer possesses a closed-form likelihood function. In this paper, we present a scalable approximate inference algorithm that lets us fit the model to high-dimensional data sets. We show on three real-world examples from different domains that in the setup of binary labels, our algorithm leads to better prediction accuracies and also selects features which show less correlation with the confounding factors.     

Component-based verification using incremental design and invariants

We propose invariant-based techniques for the efficient verification of safety and deadlock-freedom properties of component-based systems. Components and their interactions are described in the BIP language. Global invariants of composite components are obtained by combining local invariants of their constituent components with interaction invariants that take interactions into account. We study new techniques for computing interaction invariants. Some of these techniques are incremental, i.e., interaction invariants of a composite hierarchically structured component are computed by reusing invariants of its constituents. We formalize incremental construction of components in the BIP language as the process of building progressively complex components by adding interactions (synchronization constraints) to atomic components. We provide sufficient conditions ensuring preservation of invariants when new interactions are added. When these conditions are not satisfied, we propose methods for generating new invariants in an incremental manner by reusing existing invariants from the constituents in the incremental construction. The reuse of existing invariants reduces considerably the overall verification effort. The techniques have been implemented in the D-Finder toolset. Among the experiments conducted, we have been capable of verifying safety properties and deadlock-freedom of sub-systems of the functional level of the DALA autonomous robot. This work goes far beyond the capacity of existing monolithic verification tools.     

Phase-field modeling of gas bubbles and thermal conductivity evolution in nuclear fuels

A phase-field model was developed to simulate the accumulation and transport of fission products and the evolution of gas bubble microstructures in nuclear fuels. The model takes into account the generation of gas atoms and vacancies, and the elastic interaction between diffusive species and defects as well as the inhomogeneity of elasticity and diffusivity. The simulations show that gas bubble nucleation is much easier at grain boundaries than inside grains due to the trapping of gas atoms and the high mobility of vacancies and gas atoms in grain boundaries. Helium bubble formation at unstable vacancy clusters generated by irradiation depends on the mobilities of the vacancies and He, and the continuing supply of vacancies and He. The formation volume of the vacancy and He has a strong effect on the gas bubble nucleation at dislocations. The effective thermal conductivity strongly depends on the bubble volume fraction, but weakly on the morphology of the bubbles.     

3-Pyridinylidene Derivatives of Chemically Modified Lupane and Ursane Triterpenes as Promising Anticancer Agents by Targeting Apoptosis

Cancer persists as a global challenge due to the extent to which conventional anticancer therapies pose high risks counterbalanced with their therapeutic benefit. Naturally occurring substances stand as an important safer alternative source for anticancer drug development. In the current study, a series of modified lupane and ursane derivatives was subjected to in vitro screening on the NCI-60 cancer cell line panel. Compounds 6 and 7 have been identified as highly active with GI₅₀ values ranging from 0.03 µM to 5.9 µM (compound 6) and 0.18–1.53 µM (compound 7). Thus, these two compounds were further assessed in detail in order to identify a possible antiproliferative mechanism of action. DAPI (4′,6-diamidino-2-phenylindole) staining revealed that both compounds induced nuclei condensation and overall cell morphological changes consistent with apoptotic cell death. rtPCR analysis showed that both compounds induced upregulation of proapoptotic Bak and Bad genes while downregulating Bcl-XL and Bcl-2 antiapoptotic genes. Molecular docking analysis revealed that both compounds exhibited high scores for Bcl-XL inhibition, while compound 7 showed higher in silico Bcl-XL inhibition potential as compared to the native inhibitor ATB-737, suggesting that compounds may induce apoptotic cell death through targeted antiapoptotic protein inhibition, as well.     

Edible Electronics: Biocompatible and Biodegradable Materials for Organic Field‐Effect Transistors (Adv. Funct. Mater. 23/2010)

Biocompatible‐ingestible electronic circuits and capsules for medical diagnosis and monitoring are currently based on traditional silicon technology. Organic electronics has huge potential for developing biodegradable, biocompatible, bioresorbable, or even metabolizable products. An ideal pathway for such electronic devices involves fabrication with materials from nature, or materials found in common commodity products. Transistors with an operational voltage as low as 4–5 V, a source drain current of up to 0.5 μA and an on‐off ratio of 3–5 orders of magnitude have been fabricated with such materials. This work comprises steps towards environmentally safe devices in low‐cost, large volume, disposable or throwaway electronic applications, such as in food packaging, plastic bags, and disposable dishware. In addition, there is significant potential to use such electronic items in biomedical implants.