## 2 Research Method
Group 1 included the keywords \"code completion\" (variation: \"completions\"), \"code suggestion\" (variant: \"suggestions\"), and \"coding assistant\" (variation: \"assistance\"). Group 2 included \"neural\", \"convolutional\" (variant: \"convoluted\"), machine learning (variant: \"ml\"), \"bayes\" (variant: \"bayesian\"), \"intelligent\" (variant: \"intelligence\"), \"ai\" (variant: \"artificial intelligence\"), \"ai-assisted\" (variant: \"ai assisted\"), and \"ai-driven\" (variant: \"ai driven\"). Based on the keywords, a single **aggregated search string** used to perform the SLR was as follows:  
_(\"code completion\" OR \"code suggest\" OR \"coding assistant\") AND (neural OR ai OR \"artificial intelligence\" OR convolut\" OR \"machine learning\" OR bayes\" OR intelligent\" OR \"ai-assisted\" OR \"ai-driven\")_  
During the execution of the SLR, we also followed several **inclusion** and **exclusion criteria**. The literature inclusion criteria were as follows:  
* It is in the English language;
* The full text is available in a digital database;
* It is a conference paper, journal paper, or doctoral thesis;
* It is related to the supplementing or suggesting of the source code during programming and enabling techniques and tools;
* Is related to artificial intelligence methods;
* Was published in the last 4 years (2019 and later);
* Consists solely of peer-reviewed works. In the first search phase, this simply meant outdated, non-English, or non-accessible literature. The results of the SLR were as follows (please see Figure 2):  
* **Phase 1: Initial search. ** In the first phase, we performed an initial search. It returned 959 results. During our preliminary research, we discovered works [3; 11] that addressed the same domain and were published between 2019 and 2020. In order to avoid redundancy and repetition, we decided to focus on works published only in the last 4 years. Consequently, we added the rule that only those pieces of literature that were not older than four years were taken into further investigation. After eliminating the duplicates, we arrived at a total of **355** items after the first phase. * **Phase 2: Title-based screening. ** In this phase, the titles of the studies were screened independently by three researchers. The results from each researcher were then combined to ensure a comprehensive and unbiased selection. This process resulted in **62** studies appropriate for inclusion in the next phase of the review. * **Phase 3: Abstract-based screening. ** Similarly, the abstracts and keywords of the studies were reviewed independently by the same three researchers. Aftercombining the results from each researcher, **36** studies were deemed appropriate for the next phase. * **Phase 4: Full-text review. ** The full-text review was the next phase of the SLR. Directly, it provided a set of **25** primary studies. * **Phase 5: Snow-balling. ** Finally, in the last phase, additional studies were included by employing a related work review. This is how we finished the review with **28** primary studies in the field. 2. **The Survey** was the next step, aimed at gathering the developers' opinions of the usefulness of intelligent assistants. More precisely, the survey consisted of four parts. It was designed to gain insight into the developers' backgrounds and experience with information systems (ISs) development, as well as their opinions regarding the usability of intelligent assistants, their user experience with these assistants, and the likelihood of recommending intelligent assistants to others. When designing the survey, we took into account the use of established types of surveys for different areas of research. The survey was constructed as follows:  
Figure 2: SLR Progression. **Background and Experience:**  
Participants' experience with programming and IS development. Their background in using specific programming languages and development environments.