LoCo CoCo: Use the full potential of your systems engineering data!

In the previous article, I summarised our findings on communication problems in automotive Requirements Engineering (RE). While our interviewees during that studies mentioned several problems, at one of the case company they also highlighted frequently how it helped them in their daily work to have a tool that connects different kinds of data, e.g., requirements, hardware design, logical (software) design. While this clearly helps them already now to understand how their data is connected, we got the idea that this might as well be used to help them understand how people are or should be connected. This is the idea of LoCo CoCo.

LoCo CoCo (short for Low-Cost Communication and Coordination) is the idea to extract social networks from systems engineering data. That is, infer the relationship between people from items and relationships in data. Consider the example where you have a requirement, a hardware design of some sort, and two issues in your repository (see figure below). The different items could relate to each other (e.g., the hardware design contains the requirement or one of the issue relates to the requirement) and could be located in different tools (indicated by the colours). Additionally, different people might have created and/or changed those items. Based on this already existing data, we can derive a graph that connects people instead of work items. This is, in essence, what LoCo CoCo does. Using those graphs can then help engineers to find experts in a certain area (e.g., finding people knowledgeable in a particular area).

Clearly, there is related work around in the community. Two of the approaches to be mentioned in this context are Codebook and StakeSource. Ours differs in a number of ways. First, we are providing details on the implementation, whereas related work is on a rather high level. Secondly, we focus on high-level artefacts, i.e., abstract work items without a focus on (software) development. Hence, LoCo CoCo aims at early communication in an interdisciplinary environment, where code might not or not yet play a central role. Finally, we made the conscious choice not to employ any machine learning or other techniques that could introduce false positives. Instead, we intentionally only expose data that exists already in the tools. This is to avoid any false positives, but also to uncover incorrect ownership data in the systems engineering data. For example, an engineer might be listed as an owner of a requirement even though he/she is no longer in the company. In this case, an engineer consulting the social network might realise this and update the data.

We piloted LoCo CoCo at one automotive company based on data in two tools: SystemWeaver (by Systemite) and Clear Quest (IBM). While we were, so far, not able to roll out the tool, we asked practitioners at the company about the potential and their impression.

Our results show that, while LoCo CoCo has great potential, social data in systems engineering tools is often outdated, simply because it is not used. Our interviewees indicated that this might change as soon as the data is used in a meaningful way. For example, if engineers see the benefit of using social networks to contact the “right” people, they might be willing to update social data. Similarly, it could be possible to introduce simple changes in the tools to increase the data quality. For example, currently changes on systems engineering data is not logged at the case company. However, this is a common feature in most modern tools and should therefore be easy to change.

Finally, we discovered a number of ethical issues using social networks that are, to our knowledge, not commonly studied in related work. We found that the social networks created with LoCo CoCo might cause wrong perceptions when used naively. For instance, engineers might tend to believe that a person with few/no connections in a social network is not important or not working hard enough. However, the lack of connections might be related to several other factors, e.g., low data quality, a lack of trace links in the person’s work environment, or simply the role of that person. Hence, even though we only use existing data, highlighting social aspects holds a number of challenges which need to be investigated further in future work.

Based on the initial prototype, we are currently extending LoCo CoCo to extract data from OSLC adapters, one of the bigger standards for tool interoperability. Additionally, we are implementing an open source tool based on Gephi, which will include the OSLC adapters and several design considerations outlined in the current article.

(This is a summary of the article published in Information and Software Technology. If you want to know details, plus more numbers and figures, you can read it here!)

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