Using Agentic AI for contextualized and multifaceted code review at Ericsson
本文提出一种基于多智能体的代码审查方案,结合项目特定知识和专业技能,以提高代码质量,并在工业环境中验证了其有效性和准确性。
本文提出一种基于多智能体的代码审查方案,结合项目特定知识和专业技能,以提高代码质量,并在工业环境中验证了其有效性和准确性。
研究通过纵向单案例分析,探讨了敏捷软件组织中四天工作制的引入、适应、制度化及在变化条件下的持续性问题。
研究通过结合正式验证、监控和需求工程等方法,解决了公共数据交换基础设施中的数据使用控制问题,以满足隐私设计要求。
为解决学术论文中引用验证耗时问题,RefVerifier通过自动化提取引文、比对元数据及定位证据段落,辅助审稿人进行半自动化的参考文献验证。
One aspired outcome of empirical research on quantitative data is a variance theory, i.e., a quantification of the effect of an independent on a dependent variables. The validity of variance theories stems from the synthesis of multiple pieces of evidence, which increases its validity beyond the findings of a single study. However, research synthesis in SE is rare and if done mostly limited to purely narrative syntheses. At best, researchers perform meta-analyses to synthesize variance theories from several quantitative results. But even meta-analyses only produce reliable results when synthesizing exact replications yet fail to generalize from variations. We aim to extend the frontier of research synthesis beyond the state-of-the-art to systematically manage empirical evidence and its evolution. We apply method engineering to construct a framework for research synthesis from proven, individual method fragments. The framework allows researchers to put new evidence in a clear relation to an existing body of evidence and systematically expand knowledge about a studied phenomenon. We demonstrate the application of this framework to two fields of research by explicitly modeling the relationship between existing pieces of evidence. The framework puts three types of evolution of evidence into relation: (1) replications investigate the same hypothesis in a new context to improve external validity, (2) revisions challenge an existing hypothesis to improve internal validity, and (3) reanalyses replace analysis methods to improve conclusion validity. Through a systematic evolution of evidence and clear assessment criteria for each dimension of validity, the proposed framework can determine the frontier of a field of research. The framework provides a perspective to systematically evolve empirical evidence in SE, supporting more constructive and productive advances in our field.
本文提出一种基于多智能体的代码审查方案,结合项目特定知识和专业技能,以提高代码质量,并在工业环境中验证了其有效性和准确性。
研究通过纵向单案例分析,探讨了敏捷软件组织中四天工作制的引入、适应、制度化及在变化条件下的持续性问题。
研究通过结合正式验证、监控和需求工程等方法,解决了公共数据交换基础设施中的数据使用控制问题,以满足隐私设计要求。
为解决学术论文中引用验证耗时问题,RefVerifier通过自动化提取引文、比对元数据及定位证据段落,辅助审稿人进行半自动化的参考文献验证。
One aspired outcome of empirical research on quantitative data is a variance theory, i.e., a quantification of the effect of an independent on a dependent variables. The validity of variance theories stems from the synthesis of multiple pieces of evidence, which increases its validity beyond the findings of a single study. However, research synthesis in SE is rare and if done mostly limited to purely narrative syntheses. At best, researchers perform meta-analyses to synthesize variance theories from several quantitative results. But even meta-analyses only produce reliable results when synthesizing exact replications yet fail to generalize from variations. We aim to extend the frontier of research synthesis beyond the state-of-the-art to systematically manage empirical evidence and its evolution. We apply method engineering to construct a framework for research synthesis from proven, individual method fragments. The framework allows researchers to put new evidence in a clear relation to an existing body of evidence and systematically expand knowledge about a studied phenomenon. We demonstrate the application of this framework to two fields of research by explicitly modeling the relationship between existing pieces of evidence. The framework puts three types of evolution of evidence into relation: (1) replications investigate the same hypothesis in a new context to improve external validity, (2) revisions challenge an existing hypothesis to improve internal validity, and (3) reanalyses replace analysis methods to improve conclusion validity. Through a systematic evolution of evidence and clear assessment criteria for each dimension of validity, the proposed framework can determine the frontier of a field of research. The framework provides a perspective to systematically evolve empirical evidence in SE, supporting more constructive and productive advances in our field.