Increasing amount of data the organizations worldwide have at their disposal lead to the need to structure, organize and present the information obtained from it. That is because, in today's rapid-changing business environment, managers and executives need to be able to gain crucial insights about the ongoing project in as little time as possible. Recently, energy efficiency has become a greater field of research, and companies started concentrating on monitoring energy-related metrics. In addition, many of them have built their own internal tools (dashboards) to do just this. However, one of the major drawbacks of building specialized tools is the lack of adaptability. That is, they are often tailored to only one person (e.g. CEO), or a small group of them (e.g. board of directors, managers). Furthermore, the combination of metrics that is displayed to them does not change over time. This is a problem because most likely there exists a better metric combination that would allow users to get the crucial insights faster. To fill this gap, our ongoing research focuses on making the dashboards adaptable to multiple roles within the organization while optimizing for a certain goal. In some scenarios the dashboard's goal may be to detect defects, in others it may be to generate the most profit. As our primary research interest is to amplify energy efficiency, we have chosen that to be our dashboard's goal. Our previous work suggests that in order to handle compound metrics at scale it is needed to represent the dashboard as a complex system. This paper presents the design and the architecture of our proposed solution synergizing the notions from complexity theory, software architecture and user experience (UX) design.
Background: Technical Debt (TD) is a quite complex concept that includes several aspect of software development. Often, people talk about TD as the amount of postponed work but this is just a basic approximation of the concept that includes many aspects that are technical and managerial. If TD is managed properly, it can provide a huge advantage but it can also make projects unmaintainable, if not. Therefore, being able of measuring TD is a very important aspect for a proper management of the development process. However, due to the complexity of the concept and the different aspects that are involved, such measurement it not easy and there are several different approaches in literature. Goals: This work aims at investigating the existing approaches to the measurement and the analysis of TD focusing on quantitative methods that could also be automated. Method: The Systematic Literature Review (SLR) approach was applied to 331 studies obtained from the three largest digital libraries and databases. Results: After applying all filtering stages, 21 papers out of 331 were selected and deeply analyzed. The majority of them suggested new approaches to measure TD using different criteria not built on top of existing ones. Conclusions: Existing studies related to the measurement of TD were observed and analyzed. The findings have shown that the field is not mature and there are several models that have almost no independent validation. Moreover few tools for helping to automate the evaluation process exist.
The current situation when using tight time frames and frequently changing requirements when creating software dictates the need to create a system for monitoring energy consumption at any stage of production of a software product. At the first stage, we need to evaluate the state-of-the-art on this topic. To this goal, we conducted a systematic literature review. During the review more than 500 studies were observed and 124 of them were selected for detailed analysis. Among these papers, 169 metrics were derived and assessed from the point of their applicability within invasive software development process analysis. The study demonstrates the relevance of the questions posed and shows the immaturity of the area. There is no evolutionary study and the possibility of assessment at any stage of the development of a software product. The data show the importance and relevance of technical work and the importance of its further development.
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