Pragmatic evidence of cross-language link detection: A systematic literature review

Abstract

There is a rising trend for heterogeneous software applications involving multilingual source code. The key focus of reverse engineers is to unravel the cross-language links (XLLs) and their dependencies. This study aims to perform a systematic literature review (SLR) to compile different approaches, tools, techniques, and shortcomings of such techniques and understand the XLLs and their dependencies while performing reverse engineering on state-of-the-art software applications. This SLR selects 76 primary studies and uses them to create a ’go-to’ literature database, where professionals from software engineering could find all the content pertinent to the analysis and XLL detection for major multilingual applications like Java enterprise applications, Android applications, etc. It has been observed that traditional source code analysis mechanisms to reverse engineer contemporary software applications face scores of problems and limitations that need to be addressed. To assist the community in the above-mentioned goal, a general schema with definitions of XLLs and associated concepts is furnished. This study provides an SLR on XLLs, comprehensive taxonomy called cross-language analysis, which incorporates all the methods for XLL detection in multilingual source code. By pursuing future directions suggested in the end, researchers and practitioners can advance the field of multilingual applications; such as Enterprise resource planning (ERP) solutions, and cross-language software corpora, leading to improved software development practices and better understanding of language interactions in multilingual environments. The research data provided in the survey presents a comprehensive analysis of the complexities involved in working with diverse programming languages and frameworks, offering valuable insights for language technology researchers, software developers, academics, and decision-makers. This integration will enable them to identify and manage dependencies across diverse languages, leading to more efficient and reliable multilingual software systems.