Context: The Systematic Literature Review (SLR) process involves searching, selecting, and synthesizing relevant literature on a specific research topic for evidence-based decision-making in Software Engineering (SE). Due to the time-consuming of the SLR process, tool support is essential. Gap: ChatGPT is a significant advancement in Natural Language Processing (NLP), and it can potentially accelerate time-consuming and propone-error activities, such as the selection activity of the SLR process. Therefore, having a tool to assist in the selection process appears beneficial, and we argue that ChatGPT can facilitate the analysis of extensive studies, saving time and effort. Objective: We aim to evaluate the accuracy (i.e., studies correctly classified) of using ChatGPT–4.0 in SLR in SE, particularly to support the first stage, based on the title, abstract, and keywords. Method: We assessed the accuracy of utilizing ChatGPT for selecting studies, the first stage, to be included in two SLRs (SLR1 and SLR2), in contrast to the conventional method of reading the title and abstract. Results: The accuracy of ChatGPT supporting the initial selection activity was 75.3% (SLR1 – 101 correct selections: 48 inclusions and 53 exclusions; 33 incorrect selections: 17 inclusions and 16 exclusions) and 86.1% (SLR2 – 386 correct selections: 113 inclusions and 273 exclusions; 62 incorrect selections: 27 inclusions and 35 exclusions). Conclusions: Our accuracy results indicate that it is not advisable to completely outsource the selection process to ChatGPT. However, it could be valuable as a support tool, aiding novice researchers or even experienced ones when they are in doubt.

Context: Systematic mapping studies (SMS) are adopted in Software Engineering (SE) to select and synthesize relevant literature on a research topic and, thus, support evidence-based decision-making. Performing SMS is effort-demanding and time-consuming. Hence, using tools is beneficial. Large Language Models (LLMs) such as ChatGPT–4.o can potentially accelerate repetitive activities, such as data extraction in SMS, saving time and effort. Goal: We conducted this work to evaluate and provide preliminary evidence on how ChatGPT–4.o can support data extraction in SMS. Method: We performed a proof-of-concept study and assessed the results’ accuracy of using ChatGPT 4.0 to extract data in one SMS compared to the results produced manually. Results: The accuracy of ChatGPT–4.o was 87.83%. Conclusions: Our preliminary findings suggest that entirely replacing the manual data extraction with ChatGPT–4.o is not recommended. However, employing ChatGPT for semi-automated data extraction to aid in evidence synthesis in SMS is promising.

Background: Software development teams adopt various communication tools to support coordination and team interaction during the software development process. Among many other communication channels, developers' use instant messaging to discuss ideas, decisions and other project related issues with team members. Due to the informal nature of instant messaging, many of these discussions and decisions are lost. This situation could be even more critical in startups and other software companies that rely more heavily on instant message tools or other informal communication channels.Aims: This work investigates the effectiveness of using a semiautomatic approach for identifying, extracting, and determining a project's lost knowledge that was discussed using unstructured communication tools such as instant message.Methodology: We employed data-mining techniques to automatically retrieve discussions from instant message logs and showed them to the project managers to identify lost knowledge from two startup companies.Results: Our results demonstrate that the data-mining technique was capable of retrieving sentences with relevant issues discussion; reaching a precision of 75% at the first 10 relevant sentences evaluated. Moreover, the qualitative analysis conducted involving project managers shows an association of retrieved sentences with the project's lost knowledge.Conclusion: Our findings indicate that automated approaches can be used to identify such lost knowledge in software development projects. Follow-up interviews revealed the interest of PMs in adopting such automated tools in other projects.