Large Language Model-Generated Code (LLMgCode) has become increasingly common in software development. So far LLMgCode has more quality issues than Human-Authored Code (HaCode). It is common for LLMgCode to mix with HaCode in a code change, while the change is signed by only human developers, without being carefully examined. Many automated methods have been proposed to detect LLMgCode from HaCode, in which the perplexity-based method ( Perplexity for short) is the state-of-the-art method. However, the efficacy evaluation of Perplexity has focused on detection accuracy. Yet it is unclear whether Perplexity is good enough in a wider range of realistic evaluation settings. To this end, we carry out a family of experiments to compare Perplexity against feature- and pre-training-based methods from three perspectives: detection accuracy , detection speed , and generalization capability . The experimental results show that Perplexity has the best generalization capability while having limited detection accuracy and detection speed. Based on that, we discuss the strengths and limitations of Perplexity , e.g., Perplexity is unsuitable for high-level programming languages. Finally, we provide recommendations to improve Perplexity and apply it in practice. As the first large-scale investigation on detecting LLMgCode from HaCode, this article provides a wide range of findings for future improvement.

Despite the importance of conducting systematic literature reviews (SLRs) for identifying the research gaps in software engineering (SE) research, SLRs are a complex, multi-stage, and time-consuming process if performed manually. Conducting an SLR in line with the guidelines and practice in the SE domain requires considerable effort and expertise. The objective of this SLR is to identify and classify text-mining techniques and tools that can help facilitate SLR activities. This study also investigates the adoption of text-mining (TM) techniques to support SLR in the SE domain. We performed a mixed search strategy to identify relevant studies published from January 1, 2004, to December 31, 2016. We shortlisted 32 papers into the final set of relevant studies published in the SE, medicine and social science disciplines. The majority of the text-mining techniques attempted to support the study selection stage. Only 12 out of the 14 studies in the SE domain applied text-mining techniques, focusing primarily on facilitating the search and study selection stages. By learning from the experience of applying TM techniques in clinical medicine and social science fields, we believe that SE researchers can adopt appropriate SLR automation strategies for use in the SE field.

Blockchain is an emerging technology for decentralized and transactional data sharing across a large network of untrusted participants. It enables new forms of distributed software architectures, where agreement on shared states can be established without trusting a central integration point. As a database and computational platform, blockchain has both advantages and disadvantages compared with conventional techniques. Blockchain may be an appropriate choice for some use cases while conventional technologies will be more appropriate for other use cases. A major difficulty for practitioners to decide whether or not to use blockchain is that limited product data or reliable technology evaluation available to assess the suitability of blockchains. In this paper, we propose an evaluation framework that comprises a list of criteria and a typical process for practitioners to assess the suitability of applying blockchain using these criteria based on the characteristics of the use cases. We then use several existing industrial trails to evaluate the feasibility of our framework.

Incomplete and incorrect requirements may cause the safety-related software systems to fail to achieve their safety goals. It is crucial to ensure software safety by identifying proper software safety requirements during the requirements elicitation activity. Practitioners apply various Safety Risk Assessment Techniques (SRATs) to identify, analyze and assess safety risk. Nevertheless, there is a lack of guidance on how appropriate SRATs and safety process can be integrated into requirements elicitation activity to bridge the gap between the safety and requirements engineering practices. In this research, we proposed an Integration Framework that integrates safety activities and techniques into existing requirements elicitation activity.