Kristian Sandahl

An industrial survey of requirements interdependencies in software product release planning

The task of finding an optimal selection of requirements for the next release of a software system is difficult as requirements may depend on each other in complex ways. The paper presents the results from an in-depth study of the interdependencies within 5 distinct sets of requirements, each including 20 high-priority requirements of 5 distinct products from 5 different companies. The results show that: (1) roughly 20% of the requirements are responsible for 75% of the interdependencies; (2) only a few requirements are singular; (3) customer-specific bespoke development tend to include more functionality- related dependencies whereas market-driven product development have an emphasis on value-related dependencies. Several strategies for reducing the effort needed for identifying and managing interdependencies are outlined. A technique for visualization of interdependencies with the aim of supporting release planning is also discussed. The complexity of requirements interdependency analysis is studied in relation to metrics of requirements coupling. Finally, a number of issues for further research are identified.

A controlled empirical evaluation of a requirements abstraction model

Requirement engineers in industry are faced with the complexity of handling large amounts of requirements as development moves from traditional bespoke projects towards market-driven development. There is a need for usable and useful models that recognize this reality and support the engineers in a continuous effort of choosing which requirements to accept and which to dismiss off hand using the goals and product strategies put forward by management. This paper presents an evaluation of such a model that is built based on needs identified in industry. The evaluations primary goal is to test the models usability and usefulness in a lab environment prior to large scale industry piloting, and is a part of a large technology transfer effort. The evaluation uses 179 subjects from three different Swedish Universities, which is a large portion of the university students educated in requirements engineering in Sweden during 2004 and 2005. The results provide a strong indication that the model is indeed both useful and usable and ready for industry trials.

The Company Approach to Software Engineering Project Courses

Teaching larger software engineering project courses at the end of a computing curriculum is a way for students to learn some aspects of real-world jobs in industry. Such courses, often referred to as capstone courses, are effective for learning how to apply the skills they have acquired in, for example, design, test, and configuration management. However, these courses are typically performed in small teams, giving only a limited realistic perspective of problems faced when working in real companies. This paper describes an alternative approach to classic capstone projects, with the aim of being more realistic from an organizational, process, and communication perspective. This methodology, called the company approach, is described by intended learning outcomes, teaching/learning activities, and assessment tasks. The approach is implemented and evaluated in a larger Masters student course.

The Impact of Agile Principles and Practices on Large-Scale Software Development Projects: A Multiple-Case Study of Two Projects at Ericsson

BACKGROUND: Agile software development methods have a number of reported benefits on productivity, project visibility, software quality and other areas. There are also negative effects reported. However, the base of empirical evidence to the claimed effects needs more empirical studies. AIM: The purpose of the research was to contribute with empirical evidence on the impact of using agile principles and practices in large-scale, industrial software development. Research was focused on impacts within seven areas: Internal software documentation, Knowledge sharing, Project visibility, Pressure and stress, Coordination effectiveness, and Productivity. METHOD: Research was carried out as a multiple-case study on two contemporary, large-scale software development projects with different levels of agile adoption at Ericsson. Empirical data was collected through a survey of project members. RESULTS AND CONCLUSIONS: Intentional implementation of agile principles and practices were found to: correlate with a more balanced use of internal software documentation, contribute to knowledge sharing, correlate with increased project visibility and coordination effectiveness, reduce the need for other types of coordination mechanisms, and possibly increase productivity. No correlation with increase in pressure and stress were found.

Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

Bug report assignment is an important part of software maintenance. In particular, incorrect assignments of bug reports to development teams can be very expensive in large software development projects. Several studies propose automating bug assignment techniques using machine learning in open source software contexts, but no study exists for large-scale proprietary projects in industry. The goal of this study is to evaluate automated bug assignment techniques that are based on machine learning classification. In particular, we study the state-of-the-art ensemble learner Stacked Generalization (SG) that combines several classifiers. We collect more than 50,000 bug reports from five development projects from two companies in different domains. We implement automated bug assignment and evaluate the performance in a set of controlled experiments. We show that SG scales to large scale industrial application and that it outperforms the use of individual classifiers for bug assignment, reaching prediction accuracies from 50 % to 89 % when large training sets are used. In addition, we show how old training data can decrease the prediction accuracy of bug assignment. We advice industry to use SG for bug assignment in proprietary contexts, using at least 2,000 bug reports for training. Finally, we highlight the importance of not solely relying on results from cross-validation when evaluating automated bug assignment.

XML-Based Frameworks for Internet Commerce

XML-based frameworks or industry standatrds for Intemet Commerce are rapidly launched and changed. The contribution of this paper is to increase the understanding and facilitate comparison and evaluation of the most commonly refered framworks. The paper provides a survey of the architecture and message definition of BizTalk, cXML, eCo Framework, ICE (Information and Content Exchange), IOTP (Internet Open Trading Protocol), OAG (Open Applications Group), RosettaNet, xCBL, ebXML and ontology.org. The relationships between these frameworks are cooperative and competitive and thus the merger and change are unavoidable. At present eCo Framework and xCBL are tightly cooperative and supported by others. The competing initiative is centered around Microsoft’s BizTalk, supported by cXML and OAG. The future will probably see closer cooperation to make formats compatible. Microsoft is both promoting BizTalk and is a member of eCo Framework.

Using execution trace data to improve distributed systems

Understanding the dynamic behavior of a system is a key determinant to successful system maintenance. This paper contributes two studies at Ericsson Radio Systems of the perfective maintenance of large and distributed systems. Our approach is a holistic method based on tracing and the technical solution to acquisition of trace data is to use CORBA interceptors. Our method has proven useful in solving a wide variety of problems in design as well as implementation and test-all this at a small price. Examples of improvements are performance, new test cases and merging of objects.

Knowledge-based planning for protein purification

Abstract Knowledge-based systems, in particular planners, can be used to support protein purification. The problem of planning the overall purification strategy can be approached by a number of knowledge-based planning strategies. Suitable planning approaches include planning techniques emphasizing search in some plan or state space and reactive planners based on hard-coded planning schemata. An implementation of a deductive planner for protein purification is described.

An assessment model for large project courses

Larger project courses, such as capstone projects, are essential in a modern computing curriculum. Assessing such projects is, however, extremely challenging. There are various aspects and tradeoffs of assessments that can affect the quality of a project course. Individual assessments can give fair grading of individuals, but may loose focus of the project as a group activity. Extensive teacher involvement is necessary for objective assessment, but may affect the way students are working. Continuous feedback to students can enhance learning, but may be hard to combine with fair assessment. Most previous work is focusing on some specific assessment aspect, whereas we in this paper present an assessment model that consists of a collection of assessment activities, each covering different aspects. We have applied, developed, and improved these activities during a six-year period and evaluated their usefulness by performing a questionnaire-based survey.

Towards Automated Anomaly Report Assignment in Large Complex Systems Using Stacked Generalization

Maintenance costs can be substantial for organizations with very large and complex software systems. This paper describes research for reducing anomaly report turnaround time which, if successful, would contribute to reducing maintenance costs and at the same time maintaining a good customer perception. Specifically, we are addressing the problem of the manual, laborious, and inaccurate process of assigning anomaly reports to the correct design teams. In large organizations with complex systems this is particularly problematic because the receiver of the anomaly report from customer may not have detailed knowledge of the whole system. As a consequence, anomaly reports may be wrongly routed around in the organization causing delays and unnecessary work. We have developed and validated machine learning approach, based on stacked generalization, to automatically route anomaly reports to the correct design teams in the organization. A research prototype has been implemented and evaluated on roughly one year of real anomaly reports on a large and complex system at Ericsson AB. The prediction accuracy of the automation is approaching that of humans, indicating that the anomaly report handling time could be significantly reduced by using our approach.