Jeff Carver
Research Subjects
Jeff Carver has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Forrest Shull
Fraunhofer Center for Experimental Software Engineering
2 shared publications - Victor Basili
University of Maryland
2 shared publications - Guilherme Horta Travassos
Federal University of Rio de Janeiro, Brazil
1 shared publication - José Carlos Maldonado
ICMC-USP
1 shared publication - Letizia Jaccheri
Norwegian University of Science and Technology
1 shared publication - Manoel Mendonça
Federal University of Bahia (UFBa)
1 shared publication
Latest Publications
- 1 September 2004
- Published by Springer Nature in Empirical Software Engineering
- Vol. 9 (3) , 259-262
The Impact of Background and Experience on Software Inspections
- 1 September 2004
- Published by Springer Nature in Empirical Software Engineering
- Vol. 9 (3) , 259-262
The Impact of Background and Experience on Software Inspections
- 1 March 2004
- Published by Springer Nature in Empirical Software Engineering
- Vol. 9 (1-2) , 111-137
Knowledge-Sharing Issues in Experimental Software Engineering
- 1 March 2004
- Published by Springer Nature in Empirical Software Engineering
- Vol. 9 (1-2) , 111-137
Knowledge-Sharing Issues in Experimental Software Engineering
- Open Access
- 1 November 2003
- Published by Springer Nature in Journal of the Brazilian Computer Society
- Vol. 9 (2) , 77-87
Identifying implicit process variables to support future empirical work
- 1 November 2003
- Published by Springer Nature in Journal of the Brazilian Computer Society
- Vol. 9 (2) , 77-87
Open AccessIdentifying implicit process variables to support future empirical work
- Open Access
- 8 October 2003
- Published by Sociedade Brasileira de Computação
- p. 1-14
Identifying Implicit Process Variables To Support Future Empirical Work
The most basic questions that researchers must address when introducing a new process or technique are what is the intended effect of that process and can that effect be demonstrated empirically. As the understanding of a process progresses, researchers become interested in more sophisticated questions about a process or technique, such as studying the relationship between a particular type of variable and the outcome of the process. Quite often, researchers will find few, if any, studies in the literature that explicitly identify and analyze the effects of potential variables on the process. This paper proposes a methodology to aid in performing a literature search to be used as a basis for new research into these types of variables. The methodology provides guidance on making use of a large range of studies from which to extract potential variables. Throughout the paper, the methodology is illustrated with a specific example. The example focuses on searching for variables that deal with individual variations among software inspectors that affect their performance during an inspection. At the end of the example, after following the steps of the methodology, a list of potential variables among software inspectors is identified. The paper concludes with the next steps to be taken concerning the identified variables and hypotheses.
- 8 October 2003
- Published by Sociedade Brasileira de Computação
- p. 1-14
Open AccessIdentifying Implicit Process Variables To Support Future Empirical Work
The most basic questions that researchers must address when introducing a new process or technique are what is the intended effect of that process and can that effect be demonstrated empirically. As the understanding of a process progresses, researchers become interested in more sophisticated questions about a process or technique, such as studying the relationship between a particular type of variable and the outcome of the process. Quite often, researchers will find few, if any, studies in the literature that explicitly identify and analyze the effects of potential variables on the process. This paper proposes a methodology to aid in performing a literature search to be used as a basis for new research into these types of variables. The methodology provides guidance on making use of a large range of studies from which to extract potential variables. Throughout the paper, the methodology is illustrated with a specific example. The example focuses on searching for variables that deal with individual variations among software inspectors that affect their performance during an inspection. At the end of the example, after following the steps of the methodology, a list of potential variables among software inspectors is identified. The paper concludes with the next steps to be taken concerning the identified variables and hypotheses.