Context: The success or failure of any software development project significantly depends on the accuracy of its effort estimates. Software development effort estimation is the foundation for project bidding, budgeting, planning, and cost control. Problem: The literature shows that a lot of work has been done on software effort estimation. But still, there is a need for improvement in effort estimation by introducing new methodologies. The structured group-based and analogy-based effort estimations are the widely used estimation methods. Nevertheless, there are several shortcomings of using these methods such as lack of experts, lack of historical data, and biasness in expert opinion, which negatively affect the estimation results. Motivation: With the advancement of technologies, such limitations could be overcome. Such as leveraging the applicability of blockchain in several domains such as improvement in software development process and network security. Method: In this article, we have proposed a Blockchain-Based Software Effort Estimation (BBSEE) methodology to improve the software effort estimation. We employ the proposed method using Web and blockchain technologies. Moreover, we also proposed an evaluation criteria to assess the efficacy of the proposed method in terms of Mean Magnitude of Relative Error (MMRE), Mean Absolute Error (MAE), and percentage of successful predictions falling (PRED (25)). Result: We performed several case studies and analyses expert opinion of 52 organizations to present the efficacy of the proposed method. Conclusion: We observe that BBSEE method outperforms than expert judgment and analogy-based effort estimation methodologies in terms of software effort estimation.
Seyed Hossein Haeri, Peter Thompson, Neil Davies, Peter Van Roy · 6 authors
This paper directly addresses a critical issue that affects the development of many complex distributed software systems: how to establish quickly, cheaply and reliably whether they will deliver their intended performance before expending significant time, effort and money on detailed design and implementation. We describe ΔQSD, a novel metrics-based and quality-centric paradigm that uses formalised outcome diagrams to explore the performance consequences of design decisions, as a performance blueprint of the system. The ΔQSD paradigm is both effective and generic: it allows values from various sources to be combined in a rigorous way, so that approximate results can be obtained quickly and subsequently refined. ΔQSD has been successfully used by Predictable Network Solutions for consultancy on large-scale applications in a number of industries, including telecommunications, avionics, and space and defence, resulting in cumulative savings of $Bs. The paper outlines the ΔQSD paradigm, describes its formal underpinnings, and illustrates its use via a topical real-world example taken from the blockchain/cryptocurrency domain, where application of this approach enabled an advanced distributed proof-of-stake system to meet challenging throughput targets.
Seyed Hossein Haeri, Peter Thompson, Neil Davies, Peter Van Roy · 6 authors
This paper directly addresses a critical issue that affects the development of many complex distributed software systems: how to establish quickly, cheaply and reliably whether they will deliver their intended performance before expending significant time, effort and money on detailed design and implementation. We describe ÎQSD, a novel metrics-based and quality-centric paradigm that uses formalised outcome diagrams to explore the performance consequences of design decisions, as a performance blueprint of the system. The ÎQSD paradigm is both effective and generic: it allows values from various sources to be combined in a rigorous way, so that approximate results can be obtained quickly and subsequently refined. ÎQSD has been successfully used by Predictable Network Solutions for consultancy on large-scale applications in a number of industries, including telecommunications, avionics, and space and defence, resulting in cumulative savings of $Bs. The paper outlines the ÎQSD paradigm, describes its formal underpinnings, and illustrates its use via a topical real-world example taken from the blockchain/cryptocurrency domain, where application of this approach enabled an advanced distributed proof-of-stake system to meet challenging throughput targets.
N. SĂĄnchez-GĂłmez, JesĂșs Torres Valderrama, M. MejĂas, Alejandra Garrido
One of the key benefits of blockchain technology is its ability to keep a permanent, unalterable record of transactions. In business environments, where companies interact with each other without a centralized authority to ensure trust between them, this has led to blockchain platforms and smart contracts being proposed as a means of implementing trustworthy collaborative processes. Software engineers must deal with them to ensure the quality of smart contracts in all phases of the smart contract lifecycle, from requirements specifications to design and deployment. This broad scope and criticality of smart contracts in business environments means that they have to be expressed in a language that is intuitive, easy-to-use, independent of the blockchain platform employed, and oriented towards software quality assurance. In this paper we present a key component: a first outline of a UML-based smart contract meta-model that would allow us to achieve these objectives. This meta-model will be enriched in future work to represent blockchain environments and automated testing.
Abstract Classifying test executions automatically as pass or fail remains a key challenge in software testing and is referred to as the test oracle problem . It is being attempted to solve this problem with supervised learning over test execution traces. A programme is instrumented to gather execution traces as sequences of method invocations. A small fraction of the programme's execution traces is labelled with pass or fail verdicts. Execution traces are then embedded as fixed length vectors and a neural network (NN) component that uses the lineâbyâline information to classify traces as pass or fail is designed. The classification accuracy of this approach is evaluated using subject programs from different application domainsâ1. Module from Ethereum Blockchain, 2. Module from PyTorch deep learning framework, 3. Microsoft SEAL encryption library components, 4. Sed stream editor, 5. Nine network protocols from Linux packet identifier, L7âFilter and 6. Utilities library, commonsâlang for Java. For all subject programs, it was found that test execution classification had high precision, recall and specificity, averaging to 93%, 94% and 96%, respectively, while only training with an average 14% of the total traces. Experiments show that the proposed NNâbased approach is promising in classifying test executions from different application domains.
Improvement in the requirements for engineering practices is needed in areas such as requirement elicitation, validation, prioritization, and negotiations between stakeholders to create successful projects for COVID-19 (coronavirus disease 2019) software. Many algorithms and techniques are used to create quality software projects, but they still need more improvement to work effectively for global pandemic COVID-19 software. By improving the reliability of requirement engineering practices using blockchain-based technology, the software will be reliable and will make it easier for the users working in a lockdown situation because of COVID-19. Therefore, our purpose is to identify the factors for reliable software engineering practices using blockchain-oriented technology for COVID-19 software. A systematic literature review is conducted to identify challenges and offer solutions. Through using blockchain-based technology for requirement engineering practices, the requirements will be gathered accurately and validated, and the conflicts between stakeholders will also be solved. It will improve the quality and reliability of COVID-19 software projects, which will help society work effectively from home. Improvement in the quality and reliability of COVID-19 software will improve usersâ interest, and their working capacity will be increased.
Amazon Elastic Kubernetes Service (EKS) high availability demands architectural, operational, and security considerations. Results show that Kubernetes cluster durability and performance under different workloads need multi-region failover, intelligent auto-scaling, and adequate networking. Cluster auto-scaler updates and vertical and horizontal autoscaling rules improve resource efficiency and computational overhead. High Kubernetes availability requires security. Least privilege enforcement, automatic certificate rotation, RBAC, and encryption reduce data breaches, compliance violations, and illegal access. AWS KMS, Secrets Manager, and service mesh-based mutual TLS authentication safeguarded cluster communication. Observability and performance benchmarking are critical for proactive system monitoring and resilience engineering. Companies use Prometheus and Grafana for metric collecting, Fluentd and OpenTelemetry for distributed tracing, and chaos engineering frameworks for fault injection to foresee and correct issues. These methods improve enterprise MTTR, downtime, and predictability. Industry case studies prove EKS installation works. Kubemetes' scalability, dependability, and affordability aid financial institutions, healthcare, e-commerce, and telecoms. Plenty of choice Best Kubernetes practices decrease latency, infrastructure costs, and downtime. Many themes are developing for Kubemetes' high availability. Rethinking workload orchestration with serverless Kubernetes solutions like AWS Fargate for EKS simplifies infrastructure administration and resource utilization. Without node provisioning, organizations can develop quickly and inexpensively. Kubernetes-based edge computing for latency-sensitive applications including real-time analytics, 10T device orchestration, and 5G network optimization is another trend. K3s and MicroK8s at the network edge and centralized multi-cluster control planes enable regionally distributed high availability with minimal latency. Istio, Linkerd, and Consul enhance interservice communication, traffic routing, and security. Massive Kubernetes systems benefit from progressive traffic shifting, zero-downtime rolling deployments, and autonomous circuit breaking. Another achievement is Kubernetes monitoring system machine learning-driven anomaly detection and predictive maintenance. Al-powered observability frameworks can identify infrastructure deterioration, forecast resource congestion, and automate preemptive scaling to avert failures and enhance workload allocation. Kubernetes high availability optimization has improved, however Al-driven auto-scaling and self-healing clusters need research. Traditional auto-scaling ĂŒmresholds disregard non-linear workloads, traffic surges, and unexpected failures. Analysis of reinforcement learning-based scaling algorithms that dynamically adjust cluster capacity in real time utilizing historical data, prediction analytics, and workload demand predictions. Kubernetes clusters that self-heal are another autonomous infrastructure management frontier. Complexities may prevent human intervention or failover. Al-driven failure detection, automatic node repair, and intelligent fault recovery can identify abnormalities, problematic nodes, shift workloads, and take real-time remedial action in Kubernetes clusters. Studying genetic algorithms and evolutionary computing for Kubemetes scheduler development is intriguing. Least-loaded node selection schedulers and computationally efficient bin packing are not adaptive. Al-driven evolutionary scheduling models may improve Kubernetes pod placement, inter-node communication cost, and workload allocation. Cross-cluster federation and Al-augmented multi-cluster load balancing are promising research areas. Dynamic workloads need real-time adaptive load balancing in Kubernetes Federation v2. Future Al-powered predictive load distribution models may improve federated cluster job allocation by factoring network latency, computational cost, and failure likelihood. Finally, blockchain-based decentralized cloud orchestration and Kubernetes high availability intrigue. Distributed ledger technology for secure state synchronization, federated identity management, and decentralized resource scheduling may make Kubernetes more resilient to cloud provider outages, security breaches, and infrastructure failures. Blockchain-based Kubernetes topologies may increase fault tolerance, frustless multi-cloud deployments, and data integrity across geographically distant clusters. High availability computing will be defined by Al-driven automation, self-healing infrastructure, and decentralized cloud-native designs as Kubernetes use grows. These improvements may improve Kubernetes workload orchestration, system resilience, and cloud-native application scalability.
Abstract Blockchain presents a feasible method to persist immutable information in a distributed ledger to improve the level of authentication and trust. Moreover, smart contracts enable the automated execution of any contract concluded between participants of the Blockchain network. On the other hand, Microservice Architecture (MSA) is a novel approach towards service-based scalable applications. In our paper, we present an approach based on Model-Driven Engineering (MDE) that aims to facilitate the integration process of Blockchains into MSA-based applications in order to benefit from the advantages attributed to Blockchains.
Financial services must monitor their transactions to prevent being used for money laundering and combat the financing of terrorism. Initially, organizations in charge of fraud regulation were only concerned about financial institutions such as banks. However, nowadays, the Fintech industry, online businesses, or platforms involving virtual assets can also be affected by similar criminal schemes. Regardless of the differences between the entities mentioned above, malicious activities affecting them share many common patterns. This dissertation's first goal is to compile and compare existing studies involving machine learning to detect and analyze suspicious transactions. The second goal is to synthesize methodologies from the last goal for tackling different use cases in an organized manner. Finally, the third goal is to assess the applicability of deep generative models for enhancing existing solutions. In the first part of the thesis, we propose an unsupervised methodology for detecting suspicious transactions applied to two case studies. One is related to transactions from a money remittance network, and the other is related to a novel payment network based on distributed ledger technologies. Anomaly detection algorithms are applied to rank user accounts based on recency, frequency, and monetary features. The results are manually validated by domain experts, confirming known scenarios and finding unexpected new cases. In the second part, we carry out an analogous analysis employing supervised methods, along with a case study where we classify Ethereum smart contracts into honeypots and non-honeypots. We take features from the source code, the transaction data, and the funds' flow characterization. The proposed classification models proved to generalize well to unseen honeypot instances and techniques and allowed us to characterize previously unknown techniques. In the third part, we analyze the challenges that tabular data brings into the domain of deep generative models, a particular type of data used to represent financial transactions in the previous two parts. We propose a new model architecture by adapting state-of-the-art methods to output multiple variables from mixed types distributions. Additionally, we extend the evaluation metrics used in the literature to the multi-output setting, and we show empirically that our approach outperforms the existing methods. Finally, in the last part, we extend the work from the third part by applying the presented models to enhance classification tasks from the second part, commonly containing a severe class imbalance. We introduce the multi-input architecture to expand models alongside our previously proposed multi-output architecture. We compare three techniques to sample from deep generative models defining a transparent and fair large-scale experimental protocol and interesting visual analysis tools. We showed that general machine learning detection and visualization techniques could help address the fraud detection domain's many challenges. In particular, deep generative models can add value to the classification task given the imbalanced nature of the fraudulent class, in exchange for implementation and time complexity. Future and promising applications for deep generative models include missing data imputation and sharing synthetic data or data generators preserving privacy constraints.
In this paper, we describe an ongoing project to assess the liquidity risk of small and medium enterprises (SMEs) in a network. In doing so we try to mimic the tracing applications that have been done to Covid-19. We built a simple artefact under the shape of a method called LUC (Liquidity for Unstructured Collaborations) to (1) collect data that has been encrypted by using multiple keys, (2) store the data in a shared ledger and (3) extract the required information concerning the credit risk of each user while respecting the conditions for the zero-knowledge proof. We see this solution as well-adapted for firms performing unstructured collaboration and we see our service as complementary concerning a trusted company, in the same way, the Covid-19 tracing application collects weak signals and then send patients to doctors for official testing.
Santiago Figueroa-Lorenzo, Jon Goya, Javier Añorga, Iñigo AdĂn · 6 authors
The European Union is moving toward the âsmartâ era having as one of the key topics the smart mobility. What is more, the European union (EU) is moving toward Mobility as a Service (MaaS). The key concept behind MaaS is the capability to offer both the traveler's mobility and goods' transport solutions based on travel needs. For example, unique payment methods, intermodal tickets, passenger services, freight transport services, etc. The introduction of new services implies the integration of many Internet-of-Things (IoT) sensors. At this point, security gains a key role in the railway sector. Considering an environment where sensor data are monitored from sensor events, and alarms are detected and emitted when events contain an anomaly, this document proposes the development of an alarms collection system, which ensures both traceability and privacy of these alarms. This system is based on Ethereum blockchain events-log, as an efficient storage mechanism, which guarantees that any railway entity can participate in the network, ensuring both entity security and information privacy.
Jovan NikoliÄ, Nursultan Jubatyrov, Evangelos Pournaras
Large-scale decentralized systems of autonomous agents interacting via asynchronous communication often experience the following self-healing dilemma: fault detection inherits network uncertainties making a remote faulty process indistinguishable from a slow process. In the case of a slow process without fault, fault correction is undesirable as it can trigger new faults that could be prevented with fault tolerance that is a more proactive system maintenance. But in the case of an actual faulty process, fault tolerance alone without eventually correcting persistent faults can make systems underperforming. Measuring, understanding and resolving such self-healing dilemmas is a timely challenge and critical requirement given the rise of distributed ledgers, edge computing, the Internet of Things in several energy, transport and health applications. This paper contributes a novel and general-purpose modeling of fault scenarios during system runtime. They are used to accurately measure and predict inconsistencies generated by the undesirable outcomes of fault correction and fault tolerance as the means to improve self-healing of large-scale decentralized systems at the design phase. A rigorous experimental methodology is designed that evaluates 696 experimental settings of different fault scales, fault profiles and fault detection thresholds in a prototyped decentralized network of 3000 nodes. Almost 9 million measurements of inconsistencies were collected in a network, where each node monitors the health status of another node, while both can defect. The prediction performance of the modeled fault scenarios is validated in a challenging application scenario of decentralized and dynamic in-network data aggregation using real-world data from a Smart Grid pilot project. Findings confirm the origin of inconsistencies at design phase and provide new insights how to tune self-healing at an early stage. Strikingly, the aggregation accuracy is well predicted as shown by high correlations and low root mean square errors.
The use of smart contract augurs a world without intermediaries because the code and the agreements contained therein exist across a distributed, decentralized blockchain network. In software engineering, this collaboration is usually represented by using business process models and smart contracts can be used to implement business collaborations in general and inter-organizational business processes. The validation of this contract and the assurance of its quality are critical for its right application. Early testing in smart contract definition is the fact of this paper. The paper discusses the possibility to use transformation protocols to obtain derived artefacts like test case definitions and smart contract code scaffolds. Generation of derived artefacts significantly reduces the number of defects before deploying the smart contract code in the blockchain network. Transformations protocols are created using model-based software development and modelling techniques. This approach allows to simplify and improve the management and execution of collaborative business processes. This would allow, in addition, the application of systematic mechanisms to evaluate and validate the smart contract and, particularly, the application of early testing techniques which would help to reduce the number of defects and, ultimately, the cost of the final review.
The concept of microservices architecture has nowadays become popular in the development of most software systems due to their benefits of application modularity and flexibility. Nevertheless, such architecture poses new security concerns especially on how to handle APIs that act as points of communication between different services. Traditional API protection strategies, based on predetermined patterns and a centralized platform, can be ineffective in guarding microservices because of the loosely connected structure of the latter. These limitations make APIs a sweet spot of highly skilled cyber threats like unauthorized data access, injection assaults, and Distributed Denial of Service (DDoS). This research presents a conceptual framework known as Dynamic Adaptive API Security Framework that uses Artificial Intelligence (AI) and blockchain technology to address these challenges. This first one uses AI to monitor API traffic and detect anomalies in real time with the help of the proposed framework. Through anomaly detection, machine learning models can detect unusual activity such as Suspicious usage patterns, patterns with malicious payloads, and pattern of many API calls. Also, AI offers an analytic feature, which can predict the vulnerability a certain target, based on data from previous attacks, and allow targeted prevention. Alongside AI, blockchain innovation is used to create an unalterable, distributed record of communication between API. Based on consensus mechanisms like Proof of Stake or Practical Byzantine Fault Tolerance, the framework guarantees the provenance of API transaction logs. These logs offer a great resource for the forensic activities in case of a breach of the systemâs security. Also, smart contracts support even complex and constantly changing dynamic access control policies, adjusting as soon as AI-driven threat intelligence data is available. This synergy of using AI and blockchain in the framework generates an adaptable, transparent, and resilient security model that interfaces threats. Real-time anomaly detection together with immutable auditability integrated in the proposed framework improves the level of API security in microservices while simultaneously supporting GDPR and HIPAA compliance. This approach fills the gap in existing security solutions which cannot cope with the growing security issues in microservices format, providing a long-term solution for increasing security of complicated, decentralized microservices landscape. Summing up, this work presents a new comprehensive strategy to API security using the advantages of both AI and blockchain technologies. Applying the framework identifies how these technologies can be synchronously balanced and orchestrated to respond to threats, protect data input, and offer clear microservices security and foundation for the advancement of subsequent generation of software.
The last decade has sparked several valiant efforts in deductive verification of distributed agreement protocols such as consensus and leader election. Oddly, there have been far fewer verification efforts that go beyond the core protocols and target applications that are built on top of agreement protocols. This is unfortunate, as agreement-based distributed services such as data stores, locks, and ledgers are ubiquitous and potentially permit modular, scalable verification approaches that mimic their modular design. We address this need for verification of distributed agreement-based systems through our novel modeling and verification framework, QuickSilver, that is not only modular, but also fully automated. The key enabling feature of QuickSilver is our encoding of abstractions of verified agreement protocols that facilitates modular, decidable, and scalable automated verification. We demonstrate the potential of QuickSilver by modeling and efficiently verifying a series of tricky case studies, adapted from real-world applications, such as a data store, a lock service, a surveillance system, a pathfinding algorithm for mobile robots, and more.
Christopher KlinkmĂŒller, Ingo Weber, Alexander Ponomarev, An Binh Tran · 5 authors
Second generation blockchain platforms, like Ethereum, can store arbitrary data and execute user-defined smart contracts. Due to the shared nature of blockchains, understanding the usage of blockchain-based applications and the underlying network is crucial. Although log analysis is a well-established means, data extraction from blockchain platforms can be highly inconvenient and slow, not least due to the absence of logging libraries. To close the gap, we here introduce the Ethereum Logging Framework (ELF) which is highly configurable and available as open source. ELF supports users (i) in generating cost-efficient logging code readily embeddable into smart contracts and (ii) in extracting log analysis data into common formats regardless of whether the code generation has been used during development. We provide an overview of and rationale for the framework's features, outline implementation details, and demonstrate ELF's versatility based on three case studies from the public Ethereum blockchain.
Blockchain technology promises to spark a real revolution. One of most important concepts associated with this technology is smart contracts, which enable the automatic execution of agreements and augur a world without intermediaries. The conditions and rules of âcontractsâ are established in a computer codes and trust is enforced by consensus among the participants. One relevant feature associated with smart contract is the immutability property, which establishes the non-alteration of blockchain network data after the clauses of the contract are been approved by all parties or entities involved. For this reason, smart contract development requires more effort and care than the development of other common programs. They require systematic mechanisms to collect requirements and functional specifications. In addition, it is necessary to verify and validate the agreed functionality and the implemented code before they are deployed in the blockchain platform. This article presents a systematic literature review of primary studies in the field of Software Development Life Cycle, focusing on model-based software design and testing in the blockchain domain of smart contracts. This research aims to identify gaps and/or opportunities for further research. After carried out this review, it was observed that no clear methodology exists for evaluating and validating the quality either of this software or the overall development process. This means that software developers may implement smart contract code in which bugs and serious security vulnerabilities appear when the software is delivered to their customers.
Grant Chung, Luc Desrosiers, Manav Gupta, Andrew Sutton · 7 authors
Blockchain scalability can be complicated and costly. As enterprises begin to adopt blockchain technology to solve business problems, there are valid concerns if blockchain applications can support the transactional demands of production systems. In fact, the multiple distributed components and protocols that underlie blockchain applications makes performance optimization a non-trivial task. Blockchain performance optimization and scalability require a methodology to reduce complexity and cost. Furthermore, existing performance results often lack the requirements, load, and infrastructure of a production application. In this paper, we first develop a methodical approach to performance tuning enterprise blockchain applications to increase performance and transaction capacity. The methodology is applied to an enterprise blockchain-based application (leveraging Hyperledger Fabric) for performance tuning and optimization with the goal of bridging the gap between laboratory and production deployed system performance. We then present extensive results and analysis of our performance testing for on-premise and cloud deployments, in which we were able to scale the application from 30 to 3000 TPS without forking the Hyperledger Fabric source code and maintaining a reasonable infrastructure footprint. We also provide blockchain application and platform recommendations for performance improvement.
Ivan Homoliak, Sarad Venugopalan, Daniel Reijsbergen, Qingze Hum · 6 authors
Blockchains are distributed systems, in which security is a critical factor for their success. However, despite their increasing popularity and adoption, there is a lack of standardized models that study blockchain-related security threats. To fill this gap, the main focus of our work is to systematize and extend the knowledge about the security and privacy aspects of blockchains and contribute to the standardization of this domain. We propose the security reference architecture (SRA) for blockchains, which adopts a stacked model (similar to the ISO/OSI) describing the nature and hierarchy of various security and privacy aspects. The SRA contains four layers: (1) the network layer, (2) the consensus layer, (3) the replicated state machine layer, and (4) the application layer. At each of these layers, we identify known security threats, their origin, and countermeasures, while we also analyze several cross-layer dependencies. Next, to enable better reasoning about security aspects of blockchains by the practitioners, we propose a blockchain-specific version of the threat-risk assessment standard ISO/IEC 15408 by embedding the stacked model into this standard. Finally, we provide designers of blockchain platforms and applications with a design methodology following the model of SRA and its hierarchy.
The increasing digitization of financial services by the late 2010s resulted in the generation of massive volumes of transactional data across payment systems, trading platforms, digital banking applications, and regulatory reporting pipelines. These transaction logs, originally designed for auditing, reconciliation, and failure recovery, gradually emerged as a valuable source of behavioral and operational insight. However, the scale, velocity, and structural heterogeneity of transactional logs posed significant challenges to traditional analytical techniques, which were often optimized for static datasets or narrowly defined reporting use cases. As a result, organizations began exploring systematic approaches to mine patterns from transaction logs in order to better understand system behavior, detect anomalies, and improve decision-making. Pattern mining from transaction logs refers to the process of discovering recurring structures, sequences, correlations, and deviations within recorded transactional events. By September 2019, this practice was informed by a combination of data mining research, distributed systems logging techniques, and operational analytics developed in large-scale production environments. Unlike conventional business intelligence queries, pattern mining emphasizes the identification of latent relationships and temporal structures that are not explicitly encoded in application logic. These patterns may reflect normal operational workflows, emergent system behaviors, or early indicators of faults, fraud, or performance degradation. In financial systems, transaction logs capture more than simple state changes; they encode regulatory-relevant actions such as authorization decisions, settlement progressions, risk evaluations, and ledger mutations. Mining patterns from these logs enables institutions to analyze end-to-end transaction lifecycles, correlate technical events with business outcomes, and identify systemic inefficiencies or vulnerabilities. Importantly, such analysis must operate within strict constraints related to data privacy, auditability, and regulatory compliance, distinguishing transaction log mining in financial domains from analogous practices in less regulated environments. This paper examines pattern mining from transaction logs as understood and applied by September 2019, situating it within the broader evolution of logging, distributed systems observability, and data mining research. It synthesizes academic literature and industry practices to propose a conceptual and architectural framework for extracting meaningful patterns from transactional data at scale. The analysis focuses on methodological considerations, architectural layering, and practical challenges encountered in regulated, high-throughput systems, while avoiding retrospective interpretations based on post-2019 technologies or techniques.
The blockchain is a key technology that has been adopted in many application areas to increase security and reliability and to avoid the need for a central trusted authority. One of its essential underlying foundations are smart contracts, which are executable programs for managing data or assets on the blockchain. It is crucial that smart contracts are tested thoroughly due to their immutable nature and since even small bugs can lead to huge monetary losses. However, it is not enough to just test smart contracts, it is also important to ensure the quality and completeness of the tests. Hence, we introduce new smart contract specific mutation operators as well as a novel killing condition that is able to detect a deviation in the gas consumptions, i.e., in the monetary value that is required to perform transactions. Moreover, we establish a baseline for mutation testing of smart contracts by applying our method to a replay test suite and by testing about a thousand contracts.
An effective way to maximize code coverage in software tests is through dynamic symbolic execution$-$a technique that uses constraint solving to systematically explore a program's state space. We introduce an open-source dynamic symbolic execution framework called Manticore for analyzing binaries and Ethereum smart contracts. Manticore's flexible architecture allows it to support both traditional and exotic execution environments, and its API allows users to customize their analysis. Here, we discuss Manticore's architecture and demonstrate the capabilities we have used to find bugs and verify the correctness of code for our commercial clients.
Weâll see the same kind of Cambrian explosion we witnessed in the web world once we started using mutualized infrastructure in public clouds and frameworks. It took only three weeks to learn enough Ruby on Rails and Heroku to push out the first version of a management system for that brokerage. And thatâs because I had to think only about the models, the views, and the controllers. The hardest part, of course, had to do with building a secure wallet.
The rapid evolution of distributed systems during the 2010s fundamentally altered how software systems were designed, deployed, and operated, particularly in cloud-based and service-oriented environments. As organizations increasingly decomposed monolithic applications into microservices and event-driven components, traditional monitoring approaches centered on host-level metrics and reactive alerting proved insufficient. Failures became probabilistic rather than deterministic, symptoms emerged far from root causes, and system behavior could no longer be fully inferred from static architecture diagrams or predefined dashboards. Within this context, observability emerged not merely as an operational concern but as an engineering discipline that directly influences how systems are designed, instrumented, and evolved over time. Observability driven engineering refers to the practice of designing software systems such that their internal states can be inferred through externally visible signals under real-world operating conditions. By 2019, this concept had gained traction across distributed systems research and industry practice, informed by earlier control theory definitions and reinforced by practical challenges in debugging production microservices. Rather than treating telemetry as an afterthought added during operations, observability driven engineering integrates metrics, logs, and distributed traces into the development lifecycle itself, shaping interface contracts, failure semantics, and deployment strategies. This shift reflects a recognition that correctness, reliability, and performance in complex systems cannot be validated solely through pre-production testing. In regulated domains such as financial services, the need for observability carries additional significance. Payment processing systems, fraud detection pipelines, and ledger services operate under strict latency, consistency, and auditability requirements, while simultaneously being subject to partial failures, traffic bursts, and external dependencies. In such environments, the inability to explain system behavior during anomalies is not merely an inconvenience but a material operational and regulatory risk. Observability driven engineering therefore intersects with compliance obligations, incident response processes, and risk management practices, extending its relevance beyond purely technical concerns. This paper examines observability driven engineering as understood and practiced by May 2019, situating it within the broader evolution of software architecture from monolithic systems to distributed, cloud-native platforms. It synthesizes academic literature and industry experience to articulate a conceptual model for observability-aware system design, emphasizing the relationship between instrumentation, architectural layering, and operational feedback loops.