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Open research questions in Software System Performance and Reliability

137 unresolved questions extracted from the limitations and future-work sections of 326 Software System Performance and Reliability papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Developing solutions that leverage computational power to enhance data collection and analysis - Exploring the application of AcOP and AAG in other domains - Investigating methods to reduce the implementation complexity of AcOP-based implementations

    Action-Oriented Programming and Automatic Agent Generation for Adaptive Data Collection in Decentralized Data Ecosystems · 2026 · DOI

  • Conventional programming paradigms are inadequate for handling decentralized data landscapes. Manual data collection scripts are prone to errors and inefficiencies. There is a need for a novel solution that can improve modularity, adaptability, and error handling in decentralized systems.

    Action-Oriented Programming and Automatic Agent Generation for Adaptive Data Collection in Decentralized Data Ecosystems · 2026 · DOI

  • Future research should address these limitations by i) broadening fault categories to include diverse real-world anomalies, ii) deploying the framework in distributed and cloud-native production systems, and iii) integrating reinforcement-learning or deep neural models to enable predictive and autonomous fault recovery. However, the study is limited by the scope of faults tested, which focused primarily on server-side failures such as HTTP errors, memory leaks, and database disconnections.

    When Web Apps Heal Themselves: A MAPE-K Based Approach to Fault Tolerance and Adaptive Recovery · 2026

  • The search space is confined to a fixed predicate universe, - The approach relies on a custom DSL to represent UDFs, - The evaluation is limited to 150 real-world pandas and Spark data-processing pipelines

    Optimal Predicate Pushdown Synthesis · 2026 · DOI

  • Further evaluation of Pusharoo on a wider range of data-processing pipelines, - Exploration of automated refinement techniques for predicate universes, - Investigation of the applicability of Pusharoo to other domains

    Optimal Predicate Pushdown Synthesis · 2026 · DOI

  • Traditional SIEM and log aggregation systems studied here use centralized correlation and rule-based alerting; none evaluates event-driven publish-subscribe or stream-processing architectures for real-time log correlation and incident response orchestration. The papers on log analysis and SIEM deployment do not explore how decoupled event producers and consumers could improve latency, scalability, or resilience in security operations.

    CAM-LDS: cyber attack manifestations for automatic interpretation of system logs and security alerts · 2026 · DOI

  • Despite its importance, prompt engineering within SAM and its variants has not yet been systematically reviewed in the literature.

    Prompt Engineering in the Segment Anything Model: Methodologies, Applications, and Emerging Challenges · 2026 · DOI

  • The need for efficient mechanisms of message exchange between system components in modern distributed information systems. The lack of understanding of the pros and cons of RabbitMQ and Apache Kafka in terms of performance.

    Application of Message Brokers (RabbitMQ, Kafka): Performance Analysis and Use Cases · 2026 · DOI

  • The paper does not discuss performance testing under extreme or edge-case workload conditions beyond the three load levels tested (100, 1000, 10000 messages).

    Application of Message Brokers (RabbitMQ, Kafka): Performance Analysis and Use Cases · 2026 · DOI

  • The limitations of traditional monolithic architectures in achieving scalability, maintainability, and flexibility. The need for a modular design approach that promotes independent development, deployment, and scaling of application components.

    Enhancing System Modularity through Python-Based Micro services Development · 2026 · DOI

  • isolating of monolithic functionalities such as user authentication, figure rendering, and storage into independent services, the development team achieved faster deployment, improved performance, and greater fault tolerance. The use of containerization tools like Docker and orchestration platforms such as Kubernetes further streamlined deployment and scaling, while message brokers like RabbitMQ enabled efficient asynchronous communication between services. Although challenges such as data consistency, service discovery, and security remain inherent to distributed systems, modern tools and practices such as API gateways, JWT-based authentication, and centralized monitoring offer effective solutions. Overall, adopting a Python-based microservice architecture provides a strong foundation for building resilient, flexible, and future-ready applications. In conclusion, microservices empower developers to design systems that align with contemporary DevOps and cloudnative principles. By leveraging Python’s simplicity and its robust ecosystem of frameworks and tools, organizations improved can maintainability, and sustainable scalability key qualities essential for success in today’s dynamic software landscape. faster development achieve cycles, REFERENCES 1. Newman, S. (2021). Building Microservices: (2nd ed.). Designing Fine-Grained Systems O’Reilly Media. 2. Richardson, C. (2018). Microservices Patterns: With Examples in Java. Manning Publications. 3. Dragoni, N., Giallorenzo, S., Lafuente, A. L., Mazzara, M., Montesi, F., Mustafin, R., & Safina, L. (2017). Microservices: Yesterday, Today, and Tomorrow.

    Enhancing System Modularity through Python-Based Micro services Development · 2026 · DOI

  • Further evaluation of the ARCH framework in real-world cloud computing environments. Investigation of the applicability of the proposed approach to other domains, such as edge computing or IoT systems. Exploration of techniques to mitigate the trade-offs introduced by LLM-based reasoning.

    Agentic Generative AI Framework for Predictive Fault Detection in Self-Healing Cloud Environments · 2026 · DOI

  • Traditional AIOps approaches lack adaptability in dynamic environments. Existing solutions rely on predefined rules or data-driven models, limiting their effectiveness. The need for a novel approach to predictive fault detection in self-healing cloud environments.

    Agentic Generative AI Framework for Predictive Fault Detection in Self-Healing Cloud Environments · 2026 · DOI

  • The gap is the lack of a flexible and efficient system for log and data analytics. The gap is the need for a system that can handle large amounts of data quickly and efficiently.

    Dashboard Application for Log and Data Analytics · 2026 · DOI

  • The lack of a systematic approach to identify API inconsistencies in Ethereum infrastructure. The need for a specification-aware approach to generate test inputs and conduct differential testing.

    When Specifications Meet Reality: Uncovering API Inconsistencies in Ethereum Infrastructure · 2026 · DOI

  • One challenge is the need for deterministic failover, workload-aware replication, and controlled change management. Another challenge is the requirement for synchronous replication and continuous validation of availability guarantees for regulated workloads. The paper also highlights the challenge of considering the broader obligation of data platforms to public trust and institutional accountability.

    Designing Zero-Downtime, High-Availability Data Platforms for Real-Time and Regulated Systems · 2026 · DOI

  • The gap in current high-availability designs is the inability to provide zero-downtime operation for real-time and regulated systems. The gap is due to the residual tolerance for brief interruptions during planned maintenance, upgrades, and failover transitions. The paper identifies the need for a framework that can provide a foundation for designing and operating zero-downtime data platforms.

    Designing Zero-Downtime, High-Availability Data Platforms for Real-Time and Regulated Systems · 2026 · DOI

  • Evaluating optimizations of HashMap is challenging due to the complexity of factors that affect performance. Prior benchmarking methodologies have limitations such as oversimplifying workloads or being expensive to run.

    MapReplay: Trace-Driven Benchmark Generation for Java HashMap · 2026 · DOI

  • , garbage collection driven by non-map allocations, com- plex JIT interactions spanning the full application, low-level archi- tectural effects influenced by surrounding code, or multi-threaded contention and synchronization), findings from replay workloads should be validated with application benchmarks whenever such end-to-end interactions are expected to matter.

    MapReplay: Trace-Driven Benchmark Generation for Java HashMap · 2026 · DOI

  • The paper identifies a gap in existing disaster recovery strategies for hybrid financial ERP landscapes. It identifies the need for a Zero-Data-Loss strategy to ensure financial integrity and regulatory compliance.

    Zero-Data-Loss Disaster Recovery for Hybrid Financial ERP Landscapes: An AI-Driven Approach · 2026 · DOI

  • Traditional static defense mechanisms and reactive stabilization strategies lag in prediction and fail to prevent cascading failures. Contemporary system stabilization employs layered defenses, but treats them as independent components, creating critical gaps.

    Cascading Resilience Through Predictive Multi-Dimensional Safeguards: System Stability Architecture for Billion-Scale Concurrent Platforms · 2026 · DOI

  • The lack of technical expertise among non-technical users to diagnose connectivity issues. The complexity of home networks due to the rapid increase in internet-connected devices.

    Smart Network Analyzer: A Real-Time Home Network Monitoring Platform · 2026 · DOI

  • The paper identifies the challenge of scaling large language models to real-world software systems. It highlights the need for a principled approach to generating optimized code across various languages, benchmarks, and program sizes. The paper also mentions the challenge of ensuring correctness and balancing performance with maintainability in automated software optimization.

    SysLLMatic: Large language models are software system optimizers · 2026 · DOI

  • The evaluation is limited to C++ and Java, which share a C-family syntax and similar imperative programming models. The observed effectiveness of SysLLMatic may not directly generalize to languages with substantially different paradigms or runtime behaviors. There are several aspects of generalizability that are not assessed, most notably to other programming languages, application types, and hardware platforms.

    SysLLMatic: Large language models are software system optimizers · 2026 · DOI

  • Prior studies often evaluate reactive mechanisms and preventive mechanisms separately. The combined effect of live migration and rejuvenation strategies in microservice-based distributed edge storage is still unclear.

    Distributed Edge Storage Systems: Proactive High-Availability Microservices with Live Migration and Rejuvenation Strategies · 2026 · DOI

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137 open questions have been extracted from the limitations and future-work passages of 326 Software System Performance and Reliability papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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