Ashish Rajendra Sai

Abdul Razzaq, Jim Buckley, James Vincent Patten et al.

Locating bugs is an important, but effort-intensive and time-consuming task, when dealing with large-scale systems. To address this, Information Retrieval (IR) techniques are increasingly being used to suggest potential buggy source code locations, for given bug reports. While IR techniques are very scalable, in practice their effectiveness in accurately localizing bugs in a software system remains low. Results of empirical studies suggest that the effectiveness of bug localization techniques can be augmented by the configuration of queries used to locate buggy code. However, in most IR-based bug localization techniques, presented by researchers, the impact of the queries' configurations is not fully considered. In a similar vein, techniques consider all code elements as equally suspicious of being buggy while localizing bugs, but this is not always the case either.In this paper, we present a new method-level, information-retrieval-based bug localization technique called ``BoostNSift''. BoostNSift exploits the important information in queries by `boost'ing that information, and then `sift's the identified code elements, based on a novel technique that emphasizes the code elements' specific relatedness to a bug report over its generic relatedness to all bug reports. To evaluate the performance of BoostNSift, we employed a state-of-the-art empirical design that has been commonly used for evaluating file level IR-based bug localization techniques: 6851 bugs are selected from commonly used Eclipse, AspectJ, SWT, and ZXing benchmarks and made openly available for method-level analyses.

Ashish Rajendra Sai, Jim Buckley, Brian Fitzgerald et al.

Bitcoin introduced delegation of control over a monetary system from a select few to all who participate in that system. This delegation is known as the decentralization of controlling power and is a powerful security mechanism for the ecosystem. After the introduction of Bitcoin, the field of cryptocurrency has seen widespread attention from industry and academia, so much so that the original novel contribution of Bitcoin i.e. decentralization, may be overlooked, due to decentralizations assumed fundamental existence for the functioning of such cryptoassets. However recent studies have observed a trend of increased centralization in cryptocurrencies such as Bitcoin and Ethereum. As this increased centralization has an impact the security of the blockchain, it is crucial that it is measured, towards adequate control. This research derives an initial taxonomy of centralization present in decentralized blockchains through rigorous synthesis using a systematic literature review. This is followed by iterative refinement through expert interviews. We systematically analyzed 89 research papers published between 2009 and 2019. Our study contributes to the existing body of knowledge by highlighting the multiple definitions and measurements of centralization in the literature. We identify different aspects of centralization and propose an encompassing taxonomy of centralization concerns. This taxonomy is based on empirically observable and measurable characteristics. It consists of 13 aspects of centralization classified over six architectural layers Governance Network Consensus Incentive Operational and Application. We also discuss how the implications of centralization can vary depending on the aspects studied. We believe that this review and taxonomy provides a comprehensive overview of centralization in decentralized blockchains involving various conceptualizations and measures.

Ashish Rajendra Sai, Conor Holmes, Jim Buckley et al.

Blockchain systems have gained substantial traction recently, partly due to the potential of decentralized immutable mediation of economic activities. Ethereum is a prominent example that has the provision for executing stateful computing scripts known as Smart Contracts. These smart contracts resemble traditional programs, but with immutability being the core differentiating factor. Given their immutability and potential high monetary value, it becomes imperative to develop high-quality smart contracts. Software metrics have traditionally been an essential tool in determining programming quality. Given the similarity between smart contracts (written in Solidity for Ethereum) and object-oriented (OO) programming, OO metrics would appear applicable. In this paper, we empirically evaluate inheritance-based metrics as applied to smart contracts. We adopt this focus because, traditionally, inheritance has been linked to a more complex codebase which we posit is not the case with Solidity based smart contracts. In this work, we evaluate the hypothesis that, due to the differences in the context of smart contracts and OO programs, it may not be appropriate to use the same interpretation of inheritance based metrics for assessment.