Miroslaw Staron

Adopting model driven software development in industry: a case study at two companies

Model Driven Software Development (MDD) is a vision of software development where models play a core role as primary development artifacts. Its industrial adoption depends on several factors, including possibilities of increasing productivity and quality by using models. In this paper we present a case study of two companies willing to adopt the principles of MDD. One of the companies is in the process of adopting MDD while the other withdrew from its initial intentions. The results provide insights into the differences in requirements for MDD in these organizations, factors determining the decision upon adoption and the potentially suitable modeling notation for the purpose of each of the companies. The analysis of the results from this case study, supported by the conclusions from a previous case study of a successful MDD adoption, show also which conditions should be fulfilled in order to increase the chances of succeeding in adopting MDD.

A framework for developing measurement systems and its industrial evaluation

As in every engineering discipline, metrics play an important role in software development, with the difference that almost all software projects need the customization of metrics used. In other engineering disciplines, the notion of a measurement system (i.e. a tool used to collect, calculate, and report quantitative data) is well known and defined, whereas it is not as widely used in software engineering. In this paper we present a framework for developing custom measurement systems and its industrial evaluation in a software development unit within Ericsson. The results include the framework for designing measurement systems and its evaluation in real life projects at the company. The results show that with the help of ISO/IEC standards, measurement systems can be effectively used in software industry and that the presented framework improves the way of working with metrics. This paper contributes with the presentation of how automation of metrics collection and processing can be successfully introduced into a large organization and shows the benefits of it: increased efficiency of metrics collection, increased adoption of metrics in the organization, independence from individuals and standardized nomenclature for metrics in the organization.

An empirical study on using stereotypes to improve understanding of UML models

Stereotypes were introduced into the Unified Modeling Language (UML) to provide means of customizing this visual, general purpose, object-oriented modeling language, for its usage in specific application domains. The primary purpose of stereotypes is to brand an existing model element with a specific semantics. In addition, stereotypes can also be used as notational shorthand. The paper elaborates on this role of stereotypes from the perspective of UML, clarifies the role and describes a controlled experiment aimed at evaluation of the role - in the context of model understanding. The results of the experiment support the claim that stereotypes with graphical icons for their representation play a significant role in comprehension of models and show the size of the improvement.

Empirical assessment of using stereotypes to improve comprehension of UML models: A set of experiments

Stereotypes were introduced into the Unified Modeling Language to provide means of customizing this general purpose modeling language for its usage in specific application domains. The primary role of stereotypes is to brand an existing model element with specific semantics, but stereotypes can also be used to provide means of a secondary classification of modeling elements. This paper elaborates on the influence of stereotypes on the comprehension of models. The paper describes a set of controlled experiments performed in academia and industry which were aimed at evaluating the role of stereotypes in improving comprehension of UML models. The results of the experiments show that stereotypes play a significant role in the comprehension of models and the improvement achieved both by students and industry professionals.

Developing measurement systems: an industrial case study

The process of measuring in software engineering has already been standardized in the ISO/IEC 15939 standard, where activities related to identifying, creating, and evaluating of measures are described. In the process of measuring software entities, however, an organization usually needs to create custom measurement systems, which are intended to collect, analyze, and present data for a specific purpose. In this paper, we present a proven industrial process for developing measurement systems including the artifacts and deliverables important for a successful deployment of measurement systems in industry. The process has been elicited during a case study at Ericsson and is used in the organization for over 3 years when the paper was written. The process is supported by a framework that simplifies the implementation of the measurement systems and shortens the time from the initial idea to a working measurement system by the factor of 5 compared with using a standard development process not tailored for measurement systems. Copyright

Monitoring bottlenecks in agile and lean software development projects - a method and its industrial use

In the face of growing competition software projects have to deliver software products faster and with better quality - thus leaving little room for unnecessary activities or non-optimal capacity. To achieve the desired high speed of the projects and the optimal capacity,bottlenecks existing in the projects have to be monitored and effectively removed. The objective of this research is to show experiences from a mature software development organization working according to Lean and Agile software development principles. By conducting a formal case study at Ericsson we were able to elicit and automate measures required to monitor bottlenecks in software development workflow, evaluated in one of the projects. The project developed software for one of the telecom products and consisted of over 80 developers. The results of the case study include a measurement system with a number of measures/indicators which can indicate existence of bottlenecks in the flow of work in the project and a number of good practices helping other organizations to start monitoring bottlenecks in an effective way - in particular what to focus on when designing such a measurement system.

Release Readiness Indicator for Mature Agile and Lean Software Development Projects

Large companies like Ericsson increasingly often adopt the principles of Agile and Lean software development and develop large software products in iterative manner – in order to quickly respond to customer needs. In this paper we present the main indicator which is sufficient for a mature software development organization in order to predict the time in weeks to release the product. In our research project we collaborated closely with a large Agile+Lean software development project at Ericsson in Sweden. This large and mature software development project and organization has found this main indicator – release readiness – to be so important that it was used as a key performance indicator and is used in controlling the development of the product and improving organizational performance. The indicator was developed and validated in an action research project at one of the units of Ericsson AB in Sweden in one of its largest projects.

Predicting weekly defect inflow in large software projects based on project planning and test status

Defects discovered during the testing phase in software projects need to be removed before the software is shipped to the customers. The removal of defects can constitute a significant amount of effort in a project and project managers are faced with a decision whether to continue development or shift some resources to cope with defect removal. The goal of this research is to improve the practice of project management by providing a method for predicting the number of defects reported into the defect database in the project. In this paper we present a method for predicting the number of defects reported into the defect database in a large software project on a weekly basis. The method is based on using project progress data, in particular the information about the test progress, to predict defect inflow in the next three coming weeks. The results show that the prediction accuracy of our models is up to 72% (mean magnitude of relative error for predictions of 1 week in advance is 28%) when used in ongoing large software projects. The method is intended to support project managers in more accurate adjusting resources in the project, since they are notified in advance about the potentially large effort needed to correct defects.

A method for forecasting defect backlog in large streamline software development projects and its industrial evaluation

Context: Predicting a number of defects to be resolved in large software projects (defect backlog) usually requires complex statistical methods and thus is hard to use on a daily basis by practitioners in industry. Making predictions in simpler and more robust way is often required by practitioners in software engineering industry. Objective: The objective of this paper is to present a simple and reliable method for forecasting the level of defect backlog in large, lean-based software development projects. Method: The new method was created as part of an action research project conducted at Ericsson. In order to create the method we have evaluated multivariate linear regression, expert estimations and analogy-based predictions w.r.t. their accuracy and ease-of-use in industry. We have also evaluated the new method in a life project at one of the units of Ericsson during a period of 21weeks (from the beginning of the project until the release of the product). Results: The method for forecasting the level of defect backlog uses an indicator of the trend (an arrow) as a basis to forecast the level of defect backlog. Forecasts are based on moving average which combined with the current level of defect backlog was found to be the best prediction method (Mean Magnitude of Relative Error of 16%) for the level of future defect backlog. Conclusion: We have found that ease-of-use and accuracy are the main aspects for practitioners who use predictions in their work. In this paper it is concluded that using the simple moving average provides a sufficiently-good accuracy (much appreciated by practitioners involved in the study). We also conclude that using the indicator (forecasting the trend) instead of the absolute number of defects in the backlog increases the confidence in our method compared to our previous attempts (regression, analogy-based, and expert estimates).

Selecting software reliability growth models and improving their predictive accuracy using historical projects data

8 software reliability growth models are evaluated on 11 large projects.Logistic and Gompertz models have the best fit and asymptote predictions.Using growth rate from earlier projects improves asymptote prediction accuracy.Trend analysis allows choosing the best shape of the model at 50% of project time. During software development two important decisions organizations have to make are: how to allocate testing resources optimally and when the software is ready for release. SRGMs (software reliability growth models) provide empirical basis for evaluating and predicting reliability of software systems. When using SRGMs for the purpose of optimizing testing resource allocation, the models ability to accurately predict the expected defect inflow profile is useful. For assessing release readiness, the asymptote accuracy is the most important attribute. Although more than hundred models for software reliability have been proposed and evaluated over time, there exists no clear guide on which models should be used for a given software development process or for a given industrial domain.Using defect inflow profiles from large software projects from Ericsson, Volvo Car Corporation and Saab, we evaluate commonly used SRGMs for their ability to provide empirical basis for making these decisions. We also demonstrate that using defect intensity growth rate from earlier projects increases the accuracy of the predictions. Our results show that Logistic and Gompertz models are the most accurate models; we further observe that classifying a given project based on its expected shape of defect inflow help to select the most appropriate model.