## 1 Introduction  
In 2016, the building sector accounted for over 40% of the energy consumption in both the United States (US) and European Union (EU) [1]. A number of regulations exist, with an aim towards improving the standards for electricity consumption of buildings. These regulations are often framed as certifications or building models that predict the expected energy performance of proposed buildings. Despite being certified and expected to perform according to standards, buildings are often observed to underperform, thus consuming more energy than what the building has been designed for [2]. To discover and potentially address underperformance, the concept of Performance testing/Performance tests (PTing/PTs) has been created, which enables a real-time assessment and analysis of the observed behavior of the building [3; 4; 5]. A number of tests have been created, which enable comprehensive testing of the building's subsystems. The results from PTing may lead to discoveries of faults in the building, such as incorrect wiring, misconfiguration, poorly maintained and worn out components, physically broken components, or sensors and meter faults. PTing may also help detect other events that classify as underperformance, such as usage of the building against the original intention. The potential discovery of faults or underperformance in the building through PTing is definitely supporting FDD processes, too [6; 7; 8; 9]. However, the accurate diagnosis of faults is unattainable due to the PTs' lack of visibility in the internal metadata of the building. Building metadata schemas are used to store information on building components, subsystems, and their relations in a way that can be processed programmatically. Various efforts towards the development of a unified schema for building modeling exist [10; 11; 12], each designed with different uses in mind. Metadata models produced using the schema presented in [13] are a type of semantic models. There are various benefits to using semantic models, which implies that simpler relational data-based models would pale in comparison as they do not provide the same benefits. Similar attempts at semantic models intending to express buildings' hardware and the relationships between its components have been developed in Project Haystack [14]. Project Haystack deals with the development of labelling conventions (named as \"tags\") and taxonomies for buildings' operational data and hardware equipment. While used in the industry presently, Project Haystack has prevalent shortcomings such as users' ability to create arbitrary tags, which lowers the portability of Haystack models. The Smart Appliances References (SAREF) ontology is also another example of a similar ontology, used for smart homes [15]. Inspired by the aforementioned developments, the Brick metadata schema [16; 17] has been developed with a focus on completeness in vocabulary describing buildings' components and expressiveness in the relationships between them. Considering the documented superiority [17] of the Brick schema over other state of the art metadata schemas for buildings, in this paper we have chosen to work with Brick. However, buildings' metadata expression and storage are only one piece of the complex puzzle that is the assessment of a building's performance through PTing. There is a complex workflow that involves software, as well as a multitude of people with various expertise, often working independently. Due to this involvement of many parties, various errors may occur, which do not necessarily signify fault within the building. PTs are not immune to misconfiguration errors and metadata models are also not immune to incorrect data being stored within them. This paper addresses all of these types of causes for failed PTs. Combining PTing with building metadata models might advance the capabilities of PTing to diagnose and detect faults in the buildings or errors in the software-based components.