Improving Multi-Objective Test Case Selection by Injecting Diversity in Genetic Algorithms

A way to reduce the cost of regression testing consists of selecting or prioritizing subsets of test cases from a test suite according to some criteria. Besides greedy algorithms, cost cognizant additional greedy algorithms, multi-objective optimization algorithms, and Multi-Objective Genetic Algorithms (MOGAs), have also been proposed to tackle this problem. However, previous studies have shown that there is no clear winner between greedy and MOGAs, and that their combination does not necessarily produce better results. In this paper we show that the optimality of MOGAs can be significantly improved by diversifying the solutions (sub-sets of the test suite) generated during the search process. Specifically, we introduce a new MOGA, coined as DIV-GA (DIversity based Genetic Algorithm), based on the mechanisms of orthogonal design and orthogonal evolution that increase diversity by injecting new orthogonal individuals during the search process. Results of an empirical study conducted on eleven programs show that DIV-GA outperforms both greedy algorithms and the traditional MOGAs from the optimality point of view. Moreover, the solutions (sub-sets of the test suite) provided by DIV-GA are able to detect more faults than the other algorithms, while keeping the same test execution cost

Recovering Grammar Relationships for the Java Language Specification

Grammar convergence is a method that helps discovering relationships between different grammars of the same language or different language versions. The key element of the method is the operational, transformation-based representation of those relationships. Given input grammars for convergence, they are transformed until they are structurally equal. The transformations are composed from primitive operators; properties of these operators and the composed chains provide quantitative and qualitative insight into the relationships between the grammars at hand. We describe a refined method for grammar convergence, and we use it in a major study, where we recover the relationships between all the grammars that occur in the different versions of the Java Language Specification (JLS). The relationships are represented as grammar transformation chains that capture all accidental or intended differences between the JLS grammars. This method is mechanized and driven by nominal and structural differences between pairs of grammars that are subject to asymmetric, binary convergence steps. We present the underlying operator suite for grammar transformation in detail, and we illustrate the suite with many examples of transformations on the JLS grammars. We also describe the extraction effort, which was needed to make the JLS grammars amenable to automated processing. We include substantial metadata about the convergence process for the JLS so that the effort becomes reproducible and transparent

Automated identification and qualitative characterization of safety concerns reported in UAV software platforms

Unmanned Aerial Vehicles (UAVs) are nowadays used in a variety of applications. Given the cyber-physical nature of UAVs, software defects in these systems can cause issues with safety-critical implications. An important aspect of the lifecycle of UAV software is to minimize the possibility of harming humans or damaging properties through a continuous process of hazard identification and safety risk management. Specifically, safety-related concerns typically emerge during the operation of UAV systems, reported by end-users and developers in the form of issue reports and pull requests. However, popular UAV systems daily receive tens or hundreds of reports of varying types and quality. To help developers timely identifying and triaging safety-critical UAV issues, we (i) experiment with automated approaches (previously used for issue classification) for detecting the safety-related matters appearing in the titles and descriptions of issues and pull requests reported in UAV platforms, and (ii) propose a categorization of the main hazards and accidents discussed in such issues. Our results (i) show that shallow machine learning-based approaches can identify safety-related sentences with precision, recall, and F-measure values of about 80\%; and (ii) provide a categorization and description of the relationships between safety issue hazards and accidents

An NLP-based tool for software artifacts analysis

Software developers rely on various repositories and communication channels to exchange relevant information about their ongoing tasks and the status of overall project progress. In this context, semi-structured and unstructured software artifacts have been leveraged by researchers to build recommender systems aimed at supporting developers in different tasks, such as transforming user feedback in maintenance and evolution tasks, suggesting experts, or generating software documentation. More specifically, Natural Language (NL) parsing techniques have been successfully leveraged to automatically identify (or extract) the relevant information embedded in unstructured software artifacts. However, such techniques require the manual identification of patterns to be used for classification purposes. To reduce such a manual effort, we propose an NL parsingbased tool for software artifacts analysis named NEON that can automate the mining of such rules, minimizing the manual effort of developers and researchers. Through a small study involving human subjects with NL processing and parsing expertise, we assess the performance of NEON in identifying rules useful to classify app reviews for software maintenance purposes. Our results show that more than one-third of the rules inferred by NEON are relevant for the proposed task. Demo webpage: https://github.com/adisorbo/NEON too

Mechanisms of action of Methylthioadenosine: pathways implicated in neuroprotection in models of Multiple Sclerosis and other neurological diseases

From 5th European Workshop on Immune-Mediated Inflammatory Diseases (Sitges-Barcelona, Spain. 1-3 December 2010)Background Methylthioadenosine (MTA) has anti-oxidant and anti-proliferative properties and was shown to induce cell protection in hepatic cells. We previously demonstrated that exert immunomodulatory and neuroprotective effects in the animal model of Multiple Sclerosis (MS) and other neurological diseases like Parkinson disease, stroke and Epilepsy. Objective To study the mechanisms of action and different pathways implicated in the neuroprotective effect of MTA in neurological diseases. Methods RN22 (Schwnoma cell line) and PC12 (Pheochromocytoma cell line) were used to test the neuroprotective activity of MTA against stress in RN22 and to differentiate neurites in PC12. BV2 cells were used to test the effect of MTA in microglia. Organotypic cerebellum cultures were used to determine MTA effect in demyelination/remyelination. Luminex technology, western blot and ELISA were used in order to study the phosphorylated state of different pathways (AkT/PKB, ERK/MAPK, P38/SAPK or STAT3) and to determine the amount of different cytokines (IL-1β and TNF-α). Ros determination was also done by fluorescence determination. Results In vitro studies revealed that MTA protection against different stresses and its capacity to differentiate neurites implies pathways like ERK/MAPK, P38/SAPK or STAT3. MTA neuroprotective capacity is also related with its ability to reduce ROS production and oxidative stress. MTA was shown to protect against demyelination in cerebellum organotypic cultures treated with LPS or Lysolecithin. Conclusions MTA is neuroprotective in models of MS, Parkinson disease, stroke or Epilepsy. This neuroprotective effect depends on its capacity to protect against demyelination, its anti-oxidant effect and the activation of pathways related with protection against stress and production of neurite differentiation

Continuous integration and delivery practices for cyber-physical systems : an interview-based study

Continuous Integration and Delivery (CI/CD) practices have shown several benefits for software development and operations, e.g., faster release cycles and early discovery of defects. For Cyber-Physical System (CPS) development, CI/CD can help achieving required goals, such as high dependability, yet it may be challenging to apply. This paper empirically investigates challenges, barriers, and their mitigation occurring when applying CI/CD practices to develop CPSs in 10 organizations working in 8 different domains. The study has been conducted through semi-structured interviews, by applying an open card sorting procedure together with a member-checking survey within the same organizations, and by validating the results through a further survey involving 55 professional developers. The study reveals several peculiarities in the application of CI/CD to CPSs. These include the need for (i) combining continuous and periodic builds, while balancing the use of Hardware-in-the-Loop (HiL) and simulators; (ii) coping with difficulties in software deployment (iii) accounting for simulators and HiL differing in their behavior; and (vi) combining hardware/software expertise in the development team. Our findings open the road towards recommenders aimed at supporting the setting and evolution of CI/CD pipelines, as well as university curricula requiring interdisciplinarity, such as knowledge about hardware, software, and their interplay