The world has never been as unpredictable as today. Technological milestones alone are being announced almost weekly, such as large language models DeepSeek, followed by Qwen2.5, and Grok 3. Socio-political, economic, and ecological development trump technology advancements and form a complex net of interdependencies. Wars are being won and lost using technology, as are the largest wins and losses on the market, and unfortunately, impact in nature. With such a speed of development and drastic impact on all facets of our lives, the ability to predict trends, especially megatrends, is becoming essential for business, technology, politics, and daily life.In computer society, we have been making annual technology predictions for over 15 years and documenting them for 10 years (see Fig. 1). In conjunction, with the IEEE Future Directions Committee, we have been exploring technology megatrends for more than two years, issuing biennial reports. Both efforts have been releasing reports and acted in a coherent, mutually supporting role even if using different approaches (online vs inperson), scope (trends vs. megatrends, technology-only vs taking into account other megatrends), and cadence (annual vs biennial). Our reports have made a tangible impact on businesses, and education, and they are widely cited and used. In this paper, we provide a short retrospective and we outline our next steps.

Launched in 1952, the Transactions on Reliability is one of the oldest publications in the IEEE portfolio. Many papers published there have become foundational with many hundreds of citations. A listing and summary of the 10 most highly cited papers is presented.

Launched in 1952 theTransactions on Reliabilityis one of the oldest publications in the IEEE portfolio. Throughout its history, it has been led by a series of outstanding editors. The lineage and evolution of editorship of this periodical is presented.

Seventy-five years ago, visionaries in the field of reliability engineering came together to establish the IEEE Reliability Society, marking the commencement of a remarkable journey toward enhancing the dependability, resilience, and security of technological systems. As we celebrate this significant milestone, it is an opportune moment to reflect on the society's illustrious history, achievements, and the enduring impact it has had on the world of technology.

The convergence of technologies is happening across various aspects, such as communication, computing, medicine, and transportation. The smartphone is a perfect example of convergence, packing features, such as a camera, GPS, artificial intelligence, and Internet connectivity into one sleek device. Autonomous driving is another good example. In a time of rapidly converging technologies, reliability engineering must take into account the potential for cyber threats, the need for cyber trust, the importance of cyber security, and the criticality of cyber resilience. In this way, reliability engineers can ensure the confidentiality, integrity, and availability of computer systems and networks in the face of evolving threats and changing technologies. In this article, we introduce the challenges and current progress of reliability engineering in emerging technologies, including practices and applications of cyber trust and security, AI-empowered autonomous driving systems, modern mobile networks, blockchains and distributed ledger technologies, prognostic and health management, integrated circuit and hardware, and enterprise cybersecurity and threat hunting.

This study addresses two important questions related to engineering of safety-critical software and software-intensive systems. The first question is: which software and software-intensive systems should be considered safety critical? The second question is: what processes, design practices, and tools have practitioners been using for building these systems? We answer these questions through an analysis of unstructured interviews with experienced engineers who self-describe as working on safety-critical systems. Then, a thematic analysis of these responses was conducted. The results of this study are intended to provide guidance to those building safety-critical systems and have implications on state engineering licensure boards, in the determination of legal liability, and in risk assessment for policymakers, corporate governors, and insurance executives.

Despite the best intentions of software architects, it is often the case that individual developers do not faithfully implement the original security design decisions. Such a scenario sometimes leads to a situation in which while an architect claims the use of a secure architecture in the form of some tactic, the corresponding source code does not support the claim. To bridge this gap, the first critical step is to verify whether the source code reflects at least some of the structural or behavioral features required for a tactic. In this study, we examine the extent of this discrepancy between an architect's vision of what security tactics need to be adopted in the software and the actual implementation. We accomplish this research goal by 1) exploring an architect's intention to use security tactics, 2) checking whether the tactic is manifested in the design, and finally 3) recovering the evidence of efforts to implement the design in the source code. To avoid limitations to accessing documentation and source code, we use open source projects to conduct our research.

Embedded Systems design courses are important components in software, computer, and electrical engineering programs and curricula. We describe topics for inclusion in these courses and associated hands-on experiences as required portions of the courses including example development systems based upon two popular microcontrollers. We also describe the challenges of offering these courses in distance format and provide examples of how the hands-on component may be included for distance students.

In 2002, the National Institute of Standards and Technology (NIST) estimated that software defects cost the U.S. economy in the area of $60 billion a year. It is well known that identifying and tracking these defects efficiently has a measurable impact on software reliability. In this work, we evaluate 104 academic papers on defect reporting published since the NIST report to 2012 to identify the most important advancements in improving software reliability though the efficient identification and tracking of software defects. We categorize the research into the areas of automatic defect detection, automatic defect fixing, attributes of defect reports, quality of defect reports, and triage of defect reports. We then summarize the most important work being done in each area. Finally, we provide conclusions on the current state of the literature, suggest tools and lessons learned from the research for practice, and comment on open research problems.

The Annual Technical Report this year is focused on infrastructure reliability. Infrastructure constitutes those things that are apparent only in their absence. We take the infrastructure for granted, assuming it will always be there. We turn on our water facet, and drinkable water has always flowed out, for most of us, most of the time. Our infrastructure is subject to environment breakages (e.g., earthquakes), accidents (e.g., dig ups of cables), sabotage, intrusion, and compromise. Also everyday component, software or system failures can bring our infrastructure down. Our global connectivity and communications, as well as our world wide distributed development and maintenance systems, increase our productivity and efficiency, but can also increase our vulnerabilities. Our critical infrastructures can be found in many places.

When comparing software programs based on certain qualities there is usually more than one metric that can be used. Often these metrics may contradict one another or there may be no standard acceptance thresholds. In this work we demonstrate how the Analytical Hierarchy Process (AHP) can be used to mitigate the aforementioned deficiencies in metrics-based software decision making. We illustrate the procedure by incorporating value judgments from a group of experts into an existing metrics data set to rank the design complexity in three imaging software packages. In this case the injection of expert opinion in a formalized framework minimizes the problems associated with conflicting metrics. The contribution of this work is to demonstrate how a combination of expert opinion and tool-collected measures can be used to reason about software programs. The methodology employed can be easily modified to include different metrics, applications and weights, thus providing a practical assessment tool for decision making about software.

The selection of an appropriate software project management tool, as in the selection of many other tools, is often approached using an ad hoc process. Such non-rigorous approaches often based on personal preference, intuition, or marketing hype, can lead to an erroneous result. In this paper a rigorous model for selecting a software project management tool using the analytical hierarchy process (AHP) is presented. The AHP provides a flexible, systematic, and repeatable evaluation procedure that can easily be understood by the decision maker in selecting the appropriate software project management tool. Several relevant factors based on the most common features offered by commercial off-the-shelf solutions (COTS) are used as the selection criteria in ranking the software project management tools. The contribution of this work is to apply a well-known decision making procedure in a novel way to help decision makers better identify an appropriate software project management tool without having to go through a more extensive evaluation process. In addition, this work establishes a framework for comparing individual product decisions across projects, project managers, organizational groups, and organizations

In this study open source software was examined from an archaeological perspective. The objective: to explore the evolution of a long-lived open source project with the intention of learning how code evolution has progressed over time. This investigation provides a case study of Nethack, an enormously popular open source game that has been in development for more than a decade. The goal question metric approach was employed to derive measurement goals for the evaluation of open source software (OSS) evolution in Nethack. According to Lehman's laws, the incremental growth size of successive releases tends to decline during the active life of an evolving program. Our results demonstrate that the evolution patterns observed for Nethack do not consistently conform to Lehman's laws. The growth in Nethack's tarball distribution size, as well as lines of code, exhibited a linear increase in growth in this investigation. Additionally, Lehman's laws dictate that the complexity of a system will increase as it ages. Interestingly, in Nethack, the McCabe cyclomatic complexity was found to decrease with successive releases while the Halstead complexity increased. These results suggest that while the structure complexity of Nethack declined with successive releases, the complexity of calculational logic increased. These findings raise questions concerning the evolution of other OSS applications

Uncertainty pervades all aspects of engineering, and its management is of paramount importance. In software engineering, uncertainty can occur at many levels. It can appear in the software artifacts including requirements specifications, designs, and the code itself. Uncertainty can also manifest in the way we use tools, and in the engineering practices employed. It is even present in the life cycle methodologies we employ. In short, uncertainty is a persistent, negative quality of both the software and the processes that rendered it. Unfortunately, it is too easy, but often the case, that software engineers ignore issues of uncertainty or overlook them as they become marginalized within other abstractions. In this paper we propose uncertainty as a persistent software quality attribute and examine several approaches for modeling that uncertainty. Finally, we offer suggestions for future work in this regard

In all man-machine systems with image processing functions, an important unsolved problem arises in the treatment of uncertain and incomplete image information. Several frameworks have been suggested for handling uncertain image information including; expert systems, fuzzification, likelihood estimation, and neural networks. In this paper we review those methods. We also present a new method for handling uncertainties by unifying the representations of gray-values and uncertainty into one framework in a way that parallels fuzzy logic. This new framework is based on the application of the extended fuzzy pointed set and an associated algebra to handle uncertain information. We further show how this framework can be used in image processing and artificial intelligence.