Darko Durisic

Measuring the impact of changes to the complexity and coupling properties of automotive software systems

Background: In the past few decades exponential increase in the amount of software used in cars has been recorded together with enhanced requirements for functional safety of their embedded software. As the evolution of software systems in cars often entails changes to software architecture, it is important to be able to monitor their impact. Method: We conducted a case study on a distributed software system in cars at Volvo Car Corporation with the goal to develop, apply and evaluate measures of complexity and coupling which could support software architects in monitoring changes. Results: The results showed that two metrics - structural complexity and coupling measures - can guide architectural work and turn attention of architects to most complex subsystems. The results were confirmed by monitoring a complete electrical system of a vehicle under two releases. Conclusion: By applying the metrics after each significant change in the architecture, it is possible to verify that certain quality attributes have not deteriorated and to identify new testing areas. Using these metrics increases the product quality with respect to stability, reliability, and maintainability and also has potential to reduce long-term software development/maintenance costs.

Evolution of Long-Term Industrial Meta-Models -- An Automotive Case Study of AUTOSAR

Meta-models in software engineering are used to define properties of models. Therefore the evolution of the meta-models influences the evolution of the models and the software instantiated from them. The evolution of the meta-models is particularly problematic if the software has to instantiate two versions of the same meta-model - a situation common for long-term software development projects such as car development projects. In this paper, we present a case study of the evolution of the standardized meta-model used in the development of the automotive software systems - the AUTOSAR meta-model - at Volvo Car Corporation. The objective of this study is to assist automotive software designers in planning long term development projects based on multiple AUTOSAR meta-model versions. We achieve this by performing quantitative analysis of the AUTOSAR meta-model evolution in order to visualize the size and the complexity change between different meta-model versions and calculate the number of changes which need to be implemented to adopt a newer version. The analysis is done for each major role in the automotive development process affected by the changes.

Addressing the need for strict meta-modeling in practice - a case study of AUTOSAR

Meta-modeling has been a topic of interest in the modeling community for many years, yielding substantial number of papers describing its theoretical concepts. Many of them are aiming to solve the problem of traditional UML based domain-specific meta-modeling related to its non-compliance to the strict meta-modeling principle, such as the deep meta-modeling approach. In this paper, we show the practical use of meta-models in the automotive development process based on AUTOSAR and visualize places in the AUTOSAR meta-model which are broken according to the strict meta-modeling principle. We then explain how the AUTOSAR meta-modeling environment can be re-worked in order to comply to this principle by applying three individual approaches, each one combined with the concept of Orthogonal Classification Architecture: UML extension, prototypical pattern and deep instantiation. Finally we discuss the applicability of these approaches in practice and contrast the identified issues with the actual problems faced by the automotive meta-modeling practitioners. Our objective is to bridge the current gap between the theoretical and practical concerns in meta-modeling.

Identifying Optimal Sets of Standardized Architectural Features: A Method and its Automotive Application

Industrial standards are used to formalize procedures, rules and guidelines for the industry to follow. Following a standard requires continuous adoption of the new standardized features where only their subset is required by individual companies. Therefore the prioritization of the features and the assessment of their impact on the development projects is crucial for the success of the project. In software engineering, industrial standards are used increasingly often to standardize a language for designing architectural components of the system by defining domain-specific meta-models. The purpose is to assure the interoperability between a number of software tools exchanging the architectural models. In this paper, we present a method for identifying optimal sets of new standardized architectural features to be adopted in the development projects. The optimization is done based on the assessment of their benefit for the projects and the estimated cost of re-work in the modeling tools according to the changes in the standardized meta-model. We evaluate the method by applying it on 14 new architectural features of a new release of the AUTOSAR standard which is followed in the development of the automotive software systems.

Improving Measurement Certainty by Using Calibration to Find Systematic Measurement Error—A Case of Lines-of-Code Measure

Base measures such as the number of lines-of-code are often used to make predictions about such phenomena as project effort, product quality or maintenance effort. However, quite often we rely on the measurement instruments where the exact algorithm for calculating the value of the measure is not known. The objective of our research is to explore how we can increase the certainty of base measures in software engineering. We conduct a benchmarking study where we use four measurement instruments for lines-of-code measurement with unknown certainty to measure five code bases. Our results show that we can adjust the measurement values by as much as 20 % knowing the systematic error of the tool. We conclude that calibrating the measurement instruments can significantly contribute to increased accuracy in measurement processes in software engineering. This will impact the accuracy of predictions (e.g. of effort in software projects) and therefore increase the cost-efficiency of software engineering processes.

Measuring the Evolution of Meta-models - A Case Study of Modelica and UML Meta-models.

The evolution of both general purpose and domain-specific meta-models and its impact on the existing models and modeling tools has been discussed extensively in the modeling research community. To assess the impact of domain-specific meta-model evolution on the modeling tools, a number of measures have been proposed by Durisic et al., NoC (Number of Changes) being the most prominent one. The proposed measures are evaluated on a case of AUTOSAR meta-model that specifies the language for designing automotive system architectures. In this paper, we assess the applicability of these measure and the underlying data-model for their calculation in a case study of Modelica and UML meta-models. Our preliminary results show that the proposed data-model and the measures can be applied to both analyzed meta-models as we were able to capture 68/77 changes on average per Modelica/UML release. However, only a subset of the data-model elements is applicable for analyzing the evolution of Modelica and also certain transformation of the data-model is required in case of UML. Despite these encouraging results, further studies are needed to assess the usefulness of the actual measures, e.g., NoC, in assessing the impact of Modelica/UML meta-model evolution on the modeling tools.

Should We Adopt a New Version of a Standard? – A Method and Its Evaluation on AUTOSAR

The development of large software systems is usually based on a number of industrial standards that define a set of features and their requirements. In order to use new features specified in the standards, new releases of the standards need to be adopted together with their requirements. This requires a thorough impact analysis of the changes in the requirements that can be time-consuming considering their potentially high number. In order to facilitate the adoption of new releases of industrial standards in large software systems, we present a method based on both quantitative and qualitative analysis of requirements evolution. The method is evaluated in a case study of AUTOSAR - a standard used in the development of automotive software systems in cooperation with Volvo Car Group. The evaluation results show that the use of the proposed method can identify the most unstable AUTOSAR specifications and their requirements whose changes may have a significant impact on the automotive systems. This knowledge can increase the speed of adoption of new AUTOSAR releases by automotive vendors.

Co-Evolution of Meta-Modeling Syntax and Informal Semantics in Domain-Specific Modeling Environments — A Case Study of AUTOSAR

One domain-specific modeling environment is centered around a domain-specific meta-model which defines syntax (modeling elements, e.g., classes) for the domain models. However, in order for the system designers to be able to construct meaningful models, semantics of the domain-specific meta-model needs to be described as well. This semantics is often provided in a form of informal natural language specifications that contain a set of design requirements, each describing the intended use of one or more modeling elements. Intuitively, introduction of new concepts into the modeling environment is expected to require changes in both meta-modeling syntax and informal semantics in such a way that their co-evolution is highly correlated. In order to test this hypothesis, we analyzed the relation between added classes, attributes, and connectors, as meta-modeling syntax, and modified/added design requirements, as meta-modeling semantics, in a case study of the AUTOSAR meta-modeling environment. We found that new AUTOSAR concepts usually require both new modeling elements and new design requirements, but surprisingly adding more elements is not always followed by more requirements. This finding is also validated by the moderately strong correlation between the evolution of these two AUTOSAR meta-modeling artifacts (Spearmans rho 0,63 and Kendalls tau 0,49). For system designers, this means that both meta-modeling syntax and informal semantics is important to be considered in the analysis of domain-specific meta-model evolution, but it may not be enough for understanding the use of all modeling elements. For designers responsible for the maintenance of domain-specific meta-models, this means that more effort shall be put into describing the semantics of all introduced modeling elements.

Assessing the Impact of Meta-Model Evolution - A Measure and Its Automotive Application

Domain-specific meta-models play an important role in the design of large software systems by defining language for the architectural models. Such common modeling languages are particularly important if multiple actors are involved in the development process as they assure interoperability between modeling tools used by different actors. The main objective of this paper is to facilitate the adoption of new domain-specific meta-model versions, or a subset of the new architectural features they support, by the architectural modeling tools used by different actors in the development of large software systems. In order to achieve this objective, we developed a simple measure of meta-model evolution (named NoC—Number of Changes) that captures atomic modification between different versions of the analyzed meta-model. We evaluated the NoC measure on the evolution of the AUTOSAR meta-model, a domain-specific meta-model used in the design of automotive system architectures. The evaluation shows that the measure can be used as an indicator of effort needed to update meta-model-based tools to support different actors in modeling new architectural features. Our detailed results show the impact of 14 new AUTOSAR features on the modeling tools used by the main actors in the automotive development process. We validated our results by finding a significant correlation between the results of the NoC measure and the actual effort needed to support these features in the modeling tools reported by the modeling practitioners from four AUTOSAR tool vendors and the AUTOSAR tooling team at Volvo Cars. Generally, our study shows that quantitative analysis of domain-specific meta-model evolution using a simple measure such as NoC can be used as an indicator of the required updates in the meta-model-based tools that are needed to support new meta-model versions. However, our study also shows that qualitative analysis that may include an inspection of the actual meta-model changes is needed for more accurate assessment.