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Evaluation of software reliability is an important part of the process of developing modern software. Many studies are aimed at improving models for measuring and predicting the reliability of software products. However, little attention is paid to approaches to comparing existing systems in terms of software reliability. Despite the enormous importance for practice (and for managing software development), a complete and proven comparison methodology does not exist. In this article, we propose a software reliability comparison methodology in which software reliability growth models are widely used. The proposed methodology has the following features: it provides certain level of flexibility and abstraction while keeping objectivity, i.e. providing measurable comparison criteria. Also, given the comparison methodology with a set of SRGMs and evaluation criteria it becomes much easier to disseminate information about reliability of wide range of software systems. The methodology was evaluated on the example of three mobile operating systems with open source: Sailfish, Tizen, CyanogenMod.

A byproduct of our study is a comparison of the three analyzed Open Source mobile operating systems. The goal of this research is to determine which OS is stronger in terms of reliability. To this end we have performed a GQM analysis and we have identified 3 questions and 8 metrics. Considering the comparison of metrics, it appears that Sailfish is in most case the best performing OS. However, it is also the OS that performs the worst in most cases. On the contrary, Tizen scores the best in 3 cases out of 8, but the worst only in one case out of 8.

The article deals with the problem of a distributed system operation reliability. The system core is an open integration platform that provides interaction of varied software for modeling gas transportation. Some of them provide an access through thin clients on the cloud technology “software as a service”. Mathematical models of operation, transmission and computing are to ensure the operation of an automated dispatching system for oil and gas transportation. The paper presents a system solution based on the theory of Markov random processes and considers the stable operation stage. The stationary operation mode of the Markov chain with continuous time and discrete states is described by a system of Chapman–Kolmogorov equations with respect to the average numbers (mathematical expectations) of the objects in certain states. The objects of research are both system elements that are present in a large number – thin clients and computing modules, and individual ones – a server, a network manager (message broker). Together, they are interacting Markov random processes. The interaction is determined by the fact that the transition probabilities in one group of elements depend on the average numbers of other elements groups.

The authors propose a multi-criteria dispersion model of risk assessment for such systems (both in the broad and narrow sense, in accordance with the IEC standard). The risk is the standard deviation of estimated object parameter from its average value. The dispersion risk model makes possible to define optimality criteria and whole system functioning risks. In particular, for a thin client, the following is calculated: the loss profit risk, the total risk of losses due to non-productive element states, and the total risk of all system states losses.

Finally the paper proposes compromise schemes for solving the multi-criteria problem of choosing the optimal operation strategy based on the selected set of compromise criteria.

Large Language Models (LLMs) are transforming software engineering by bridging the gap between natural language and programming languages. These models have revolutionized communication within development teams and the Software Development Life Cycle (SDLC) by enabling developers to interact with code using natural language, thereby improving workflow efficiency. This survey examines the impact of LLMs across various stages of the SDLC, including requirement gathering, system design, coding, debugging, testing, and documentation. LLMs have proven to be particularly useful in automating repetitive tasks such as code generation, refactoring, and bug detection, thus reducing manual effort and accelerating the development process. The integration of LLMs into the development process offers several advantages, including the automation of error correction, enhanced collaboration, and the ability to generate high-quality, functional code based on natural language input. Additionally, LLMs assist developers in understanding and implementing complex software requirements and design patterns. This paper also discusses the evolution of LLMs from simple code completion tools to sophisticated models capable of performing high-level software engineering tasks. However, despite their benefits, there are challenges associated with LLM adoption, such as issues related to model accuracy, interpretability, and potential biases. These limitations must be addressed to ensure the reliable deployment of LLMs in production environments. The paper concludes by identifying key areas for future research, including improving the adaptability of LLMs to specific software domains, enhancing their contextual understanding, and refining their capabilities to generate semantically accurate and efficient code. This survey provides valuable insights into the evolving role of LLMs in software engineering, offering a foundation for further exploration and practical implementation.