Hussein Alrubaye

Eman Abdullah AlOmar, Hussein AlRubaye, Mohamed Wiem Mkaouer et al.

Modern code review is a common and essential practice employed in both industrial and open-source projects to improve software quality, share knowledge, and ensure conformance with coding standards. During code review, developers may inspect and discuss various changes including refactoring activities before merging code changes in the codebase. To date, code review has been extensively studied to explore its general challenges, best practices and outcomes, and socio-technical aspects. However, little is known about how refactoring activities are being reviewed, perceived, and practiced. This study aims to reveal insights into how reviewers develop a decision about accepting or rejecting a submitted refactoring request, and what makes such review challenging. We present an industrial case study with 24 professional developers at Xerox. Particularly, we study the motivations, documentation practices, challenges, verification, and implications of refactoring activities during code review. Our study delivers several important findings. Our results report the lack of a proper procedure to follow by developers when documenting their refactorings for review. Our survey with reviewers has also revealed several difficulties related to understanding the refactoring intent and implications on the functional and non-functional aspects of the software. In light of our findings, we recommended a procedure to properly document refactoring activities, as part of our survey feedback.

Hussein Alrubaye, Deema Alshoaibi, Eman Alomar et al.

The migration process between different third-party software libraries is hard, complex and error-prone. Typically, during a library migration process, developers opt to replace methods from the retired library with other methods from a new library without altering the software behavior. However, the extent to which such a migration process to new libraries will be rewarded with an improved software quality is still unknown. In this paper, we aim at studying and analyzing the impact of library API migration on software quality. We conduct a large-scale empirical study on 9 popular API migrations, collected from a corpus of 57,447 open-source Java projects. We compute the values of commonly-used software quality metrics before and after a migration occurs. The statistical analysis of the obtained results provides evidence that library migrations are likely to improve different software quality attributes including significantly reduced coupling, increased cohesion, and improved code readability. Furthermore, we release an online portal that helps software developers to understand the pre-impact of a library migration on software quality and recommend migration examples that adopt the best design and implementation practices to improve software quality. Finally, we provide the software engineering community with a large scale dataset to foster research in software library migration.

Hussein Alrubaye, Mohamed Wiem Mkaouer, Ali Ouni

In this paper we introduce, MigrationMiner, an automated tool that detects code migrations performed between Java third-party library. Given a list of open source projects, the tool detects potential library migration code changes and collects the specific code fragments in which the developer replaces methods from the retired library with methods from the new library. To support the migration process, MigrationMiner collects the library documentation that is associated with every method involved in the migration. We evaluate our tool on a benchmark of manually validated library migrations. Results show that MigrationMiner achieves an accuracy of 100%. A demo video of MigrationMiner is available at https://youtu.be/sAlR1HNetXc.

Hussein Alrubaye, Mohamed Wiem Mkaouer, Igor Khokhlov et al.

The manual migration between different third-party libraries represents a challenge for software developers. Developers typically need to explore both libraries Application Programming Interfaces, along with reading their documentation, in order to locate the suitable mappings between replacing and replaced methods. In this paper, we introduce RAPIM, a novel machine learning approach that recommends mappings between methods from two different libraries. Our model learns from previous migrations, manually performed in mined software systems, and extracts a set of features related to the similarity between method signatures and method textual documentation. We evaluate our model using 8 popular migrations, collected from 57,447 open-source Java projects. Results show that RAPIM is able to recommend relevant library API mappings with an average accuracy score of 87%. Finally, we provide the community with an API recommendation web service that could be used to support the migration process.

Hussein Alrubaye, Mohamed Wiem Mkaouer, Ali Ouni

The migration process between different third-party libraries is hard, complex and error-prone. Typically, during a library migration, developers need to find methods in the new library that are most adequate in replacing the old methods of the retired library. This process is subjective and time-consuming as developers need to fully understand the documentation of both libraries Application Programming Interfaces, and find the right matching between their methods if it exists. In this context, several studies rely on mining existing library migrations to provide developers with by-example approaches for similar scenarios. In this paper, we introduce a novel mining approach that extracts existing instances of library method replacements that are manually performed by developers for a given library migration to automatically generate migration patterns in the method level. Thereafter, our approach combines the mined method-change patterns with method-related lexical similarity to accurately detect mappings between replacing/replaced methods. We conduct a large scale empirical study to evaluate our approach on a benchmark of 57,447 open-source Java projects leading to 9 popular library migrations. Our qualitative results indicate that our approach significantly increases the accuracy of mining method-level mappings by an average accuracy of 12%, as well as increasing the number of discovered method mappings, in comparison with existing state-of-the-art studies. Finally, we provide the community with an open source mining tool along with a dataset of all mined migrations at the method level.