Behind the Intents: An In-depth Empirical Study on Software Refactoring in Modern Code Review

Code refactorings are of pivotal importance in modern code review. Developers may preserve, revisit, add or undo refactorings through changes’ revisions. Their goal is to certify that the driving intent of a code change is properly achieved. Developers’ intents behind refactorings may vary from pure structural improvement to facilitating feature additions and bug fixes. However, there is little understanding of the refactoring practices performed by developers during the code review process. It is also unclear whether the developers’ intents influence the selection, composition, and evolution of refactorings during the review of a code change. Through mining 1,780 reviewed code changes from 6 systems pertaining to two large open-source communities, we report the first in-depth empirical study on software refactoring during code review. We inspected and classified the developers’ intents behind each code change into 7 distinct categories. By analyzing data generated during the complete reviewing process, we observe: (i) how refactorings are selected, composed and evolved throughout each code change, and (ii) how developers’ intents are related to these decisions. For instance, our analysis shows developers regularly apply non-trivial sequences of refactorings that crosscut multiple code elements (i.e., widely scattered in the program) to support a single feature addition. Moreover, we observed that new developers’ intents commonly emerge during the code review process, influencing how developers select and compose their refactorings to achieve the new and adapted goals. Finally, we provide an enriched dataset that allows researchers to investigate the context and motivations behind refactoring operations during the code review process

The link between transformational and servant leadership in DevOps-oriented organizations

DevOps is a set of agile and lean practices and principles in the context of software product development aiming to decrease mean time-to-market and mean time-to-recover-from-failure through a shift in organizational mindset-skillset-toolset. There is literature to suggest that adopting DevOps has been challenging in practice and that a particular leadership style is necessary to lead DevOps adoption. There are studies to suggest that DevOps leadership is mainly related to transformational leadership characteristics. In this research, a mixed methods approach is used. Initially, semi-structured interviews are conducted with 30 EMEA (Europe, Middle-East and Africa) agile and lean practitioners holding more than 10 years of practitioner experience (81%) from the private and public sectors. The contribution also includes an analysis and evaluation of a survey completed by 250 participants of which 93% works in Europe and Middle East and 76% has held previous leadership positions. By looking to recent literature we identified agile, lean and DevOps practices and principles. In addition, we identify benefits and inhibitors to DevOps adoption and its leadership. Our results suggest that deep rooted organizational culture and lack of DevOps definition clarity are usually considered impediments to DevOps adoption followed by poor communication and collaboration. Our results also show that certain DevOps adoption leadership characteristics are relevant to transformational leadership and servant leadership. The research results also indicate that the DevOps adoption leadership role is linked to certain metrics

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen