The rapid growth of software industry has led to a significant increase in the production of a variety of data during software development process, highlighting the apparent need for improved data quality management. As an effective means of software process research and practice, Software Process Simulation Modeling (SPSM) requires large amount and high quality data that precisely depicts what happens during the development process. Accordingly, process simulation models can be used as a reference framework for assessing the issues in data management and data governance from a process perspective. The objective of the work reported in this paper is to provide insights into the data issues in real-world industrial settings and the corresponding coping strategies for software process modelers in particular in order to assist them in preparing and assessing data for their simulation models when conducting effective SPSM in the real-world settings. This paper reports on an empirical investigation that applies software process simulation practices to study the data issues and the data governance strategies based on an industrial case from one global ICT enterprise. As the outcome, a refined process for data preparation is presented, along with a taxonomy of the data issues and the corresponding coping strategies. This paper also explores traceability recovery approaches to mine more accurate process state information from software artifacts and analyzes the impact of the recovered data traceability information by evaluating the improved fidelity of the process simulation model.

The traceability of software artifacts has been recognized as an important factor to support various activities in software development processes. However, traceability can be difficult and time-consuming to create and maintain manually, thereby automated approaches have gained much attention. Unfortunately, existing automated approaches for traceability suffer from practical issues. This paper aims to gain an understanding of the potential challenges for the underperforming of the state-of-the-art, ML-based trace link classifiers applied in real-world projects. By investigating different industrial datasets, we found that two critical (and classic) challenges, i.e. data imbalance and sparse problems, lie in real-world projects’ traceability automation. To overcome these challenges, we developed a framework called SPLINT to incorporate hybrid textual similarity measures and semi-supervised learning strategies as enhancements to the learning-based traceability approaches. We carried out experiments with six open-source platforms and ten industry datasets. The results confirm that SPLINT is able to operate at higher performance on two communities’ datasets. Specifically, the industrial datasets, which significantly suffer from data imbalance and sparsity problems, show an increase in F2-score over 14% and AUC over 8% on average. The adjusted class-balancing and self-training policies used in SPLINT (CBST-Adjust) also work effectively for the selection of pseudo-labels on minor classes from unlabeled trace sets, demonstrating SPLINT’s practicability.

Despite widespread agreement on the benefits of code review, its outcomes may not be as expected. The complications can undermine the purpose of the development process and even destroy the entire development cycle. Both academia and the industrial communities have invested a great deal of time and effort into code reviews. When a project team adheres to the best practices and creates a conducive environment, it is likely that code reviews could be conducted effectively and efficiently. By reviewing peer-reviewed scientific publications and gray literature on code review best practices, we summarized 57 practices as well as 19 code review pains that they address. Our review has shown that following best practices can ease the process of code review considerably. Multiple actionable practices are needed to support code review pains at the same time. To enable the adoption of best practices, OSS and industrial communities alike invest in integrating automatic techniques with code review tools. We hope that this review will provide researchers and practitioners with a comprehensive understanding of code review practices, aiding them in conducting code reviews more successfully.