1,721,116 research outputs found
Experimenting with error abstraction in requirements documents
No full textIn previous experiments we showed that the Perspective-Based Reading (PBR) family of defect detection techniques was effective at detecting faults in requirements documents in some contexts. Experiences from these studies indicate that requirements faults are very difficult to define, classify and quantify. In order to address these difficulties, we present an empirical study whose main purpose is to investigate whether defect detection in requirements documents can be improved by focusing on the errors (i.e., underlying human misconceptions) in a document rather than the individual faults that they cause. In the context of a controlled experiment, we assess both benefits and costs of the process of abstracting errors from faults in requirements documents
Investigating maintenance processes in a framework-based environment
No full textThe empirical study described in this paper focuses on the effectiveness of maintenance processes in an environment in which a repository of potential sources of reuse exists, e.g. a context in which applications are built using an object-oriented framework. Such a repository might contain current and previous releases of the system under maintenance, as well as other applications that are built on a similar structure or contain similar functionality. This paper presents an observational study of 15 student projects in a framework-based environment. We used a mix of qualitative and quantitative methods to identify and evaluate the effectiveness of the maintenance processes
Building Knowledge through Families of Experiments
No full textExperimentation in software engineering is necessary but difficult. One reason is that there are a large number of context variables and, so, creating a cohesive understanding of experimental results requires a mechanism for motivating studies and integrating results. It requires a community of researchers that can replicate studies, vary context variables, and build models that represent the common observations about the discipline. This paper discusses the experience of the authors, based upon a collection of experiments, in terms of a framework for organizing sets of related studies. With such a framework, experiments can be viewed as part of common families of studies, rather than being isolated events. Common families of studies can contribute to important and relevant hypotheses that may not be suggested by individual experiments. A framework also facilitates building knowledge in an incremental manner through the replication of experiments within families of studies. To support the framework, this paper discusses the experiences of the authors in carrying out empirical studies, with specific emphasis on persistent problems encountered in experimental design, threats to validity, criteria for evaluation, and execution of experiments in the domain of software engineering
Investigating Technical Debt Folklore - Shedding some light on technical debt opinion
Full text linkWe identified and organized a number of statements about technical debt (TD Folklore list) expressed by practitioners in online websites, blogs and published papers. We chose 14 statements and we evaluated them through two surveys (37 practitioners answered the questionnaires), ranking them by agreement and consensus. The statements most agreed with show that TD is an important factor in software project management and not simply another term for “bad code”. This study will help the research community in identifying folklore that can be translated into research questions to be investigated, thus targeting attempts to provide a scientific basis for TD management
On failure classification: The impact of "getting it wrong"
No full textBug classification is a well-established practice which supports important activities such as enhancing verification and validation (V&V) efficiency and effectiveness. The state of the practice is manual and hence classification errors occur. This paper investigates the sensitivity of the value of bug classification (specifically, failure type classification) to its error rate; i.e., the degree to which misclassified historic bugs decrease the V&V effectiveness (i.e., the ability to find bugs of a failure type of interest). Results from the analysis of an industrial database of more than 3,000 bugs show that the impact of classification error rate on V&V effectiveness significantly varies with failure type. Specifically, there are failure types for which a 5% classification error can decrease the ability to find them by 66%. Conversely, there are failure types for which the V&V effectiveness is robust to very high error rates. These results show the utility of future research aimed at: 1) providing better tool support for decreasing human errors in classifying the failure type of bugs, 2) providing more robust approaches for the selection of V&V techniques, and 3) including robustness as an important criterion when evaluating technologies. Copyright © 2014 ACM
A checklist for integrating student empirical studies with research and teaching goals
No full textThe use of empirical studies with students in software engineering helps researchers gain insight into new or existing techniques and methods. However, due mainly to concerns of external validity, questions have been raised about the value of these types of studies. The authors of this paper draw on their experiences of conducting a large number of empirical studies in university courses in three countries (Italy, Norway, and the United States) to address this important issue. This paper first identifies the requirements that research and pedagogy place on a valid empirical study with students. This information is then used as the basis for a checklist that provides guidance for researchers and educators when planning and conducting studies in university courses. The goal of this checklist is to help ensure that these studies have as much research and pedagogical value as possible. Finally, an example application of the checklist is provided to illustrate its use
Technical debt: Showing the way for better transfer of empirical results
No full textIn this chapter, we discuss recent progress and opportunities in empirical software engineering by focusing on a particular technology, Technical Debt (TD), which ties together many recent developments in the field. Recent advances in TD research are providing empiricists the chance to make more sophisticated recommendations that have observable impact on practice. TD uses a financial metaphor and provides a framework for articulating the notion of tradeoffs between the short-term benefits and the long-term costs of software development decisions. TD is seeing an explosion of interest in the practitioner community, and research in this area is quickly having an impact on practice. We argue that this is due to several strands of empirical research reaching a level of maturity that provides useful benefits to practitioners, who in turn provide excellent data to researchers. They key is providing observable benefit to practitioners, such as the ability to tie technical debt measures to business goals, and the ability to articulate more sophisticated value-based propositions regarding how to prioritize rework. TD is an interesting case study in how the maturing field of empirical software engineering research is paying dividends. It is only a little hyperbolic to call this a watershed moment for empirical study, where many areas of progress are coming to a head at the same time
- …
