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Bug triaging is a critical and time-consuming activity of software maintenance. This paper aims to present an automated heuristic approach combined with fuzzy multi-criteria decision-making for bug triaging. To date, studies lack consideration of multi-criteria inputs to gather decisive and explicit knowledge of bug reports. The proposed approach builds a bug priority queue using the multi-criteria fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method and combines it with Bacterial Foraging Optimization Algorithm (BFOA) and Bar Systems (BAR) optimization to select developers. A relative threshold value is computed and categorization of developers is performed using hybrid optimization techniques to make a distinction between active, inactive, or new developers for bug allocation. The harmonic mean of precision, recall, f-measure, and accuracy obtained is 92.05%, 89.21%, 85.09%, and 93.11% respectively. This indicates increased overall accuracy of 90%±2% when compared with existing approaches. Overall, it is a novel solution to improve the bug assignment process which utilizes intuitive judgment of triagers using fuzzy multi-criteria decision making and is capable of making a distinction between active, inactive, and new developers based on their relative workload categorization.

Virtual machine placement and migration (VMPM) are key operations for managing cloud resources. Considering the large scale of cloud infrastructures, several proposals still fail to provide a comprehensive and scalable solution. A variety of approaches have been used to address this issue, e.g., the modern portfolio theory (MPT). Originally formulated for financial markets, MPT enables the construction of a portfolio of financial assets in order to maximize profit and reduce risk. This paper presents a novel VMPM approach applying MPT and incremental statistics computation for VMPM decision-making so as to maximize resource usage while minimizing under and overload. Simulation experiments were conducted using CloudSim Plus, relying on synthetic data, PlanetLab and Google Cluster traces. Results show that the proposal is highly scalable and largely reduces computational complexity and memory footprint, making it suitable for large-scale cloud service providers.

The adoption and popularization of mobile devices, such as smartphones and tablets, accentuated after the second decade of this century, has been motivated by the growing number of mobile applications, which can solve problems in different areas of contemporary societies. Conversely, the software development industry is motivated by the increasing number and quality of resources that mobile devices possess nowadays (e.g., memory, sensors, processing power or battery). While powerful mobile devices do exist, one of the main driving factors behind the increase of resources is the usage of Cloud technology, which strongly complement mobile computing. As expected, the adoption of measures to mitigate security issues has not accompanied the growth and speed of development for Cloud and Mobile software, to ensure that these are resilient to attacks by design. Aiming to contribute to decrease the gap between software and security engineering, this paper presents a deep approach to attack taxonomy, security mechanisms, and security test specification for the Cloud and Mobile ecosystem of applications. This is also the first time an encompassing and conjoined approach is provided for attack taxonomy and specification of security tests automation tools for this ecosystem.