V. Basili and D. Weiss. A methodology for collecting valid software engineering data, IEEE Transactions on Software Engineering, SE-10 (6) (1984) 728-738.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....Hypothesis 2: Changing modules that are tightly coupled to each other via data and control flow relationships requires more effort than changing modules that are loosely coupled to each other. 2. 1 Design The case study to test our hypotheses was designed using the Goal Question Metric Paradigm [BW84, BR88]. Our G Q M goal was to analyze two FOR TRAN systems for the purpose of characterizing them with respect to the influence of design complexity on the maintainability of modules, from the point of view of the researchers within the context of the SEL. We analyzed vertical design complexity ....

Victor R. Basili and David M. Weiss. A methodology for collecting valid software engineering data. IEEE Transactions on Software Engineering, SE-10(6):728--738, November 1984.

....Hypothesis 2: Changing modules that are tightly coupled to each other via data and control flow relationships requires more effort than changing modules that are loosely coupled to each other. 2. 1 Design The case study to test our hypotheses was designed using the Goal Question Metric Paradigm [BW84,BR88]. Our G Q M goal was to analyze two FORTRAN systems for the purpose of characterizing them with respect to the influence of design complexity on the maintainability of modules, from the point of view of the researchers within the context of the SEL. We analyzed vertical design complexity ....

Victor R. Basili and David M. Weiss. A methodology for collecting valid software engineering data. IEEE Transactions on Software Engineering, SE-10(6):728--738, November 1984.

....of process metrics to be collected and analyzed. Metrics are selected based on the project s process requirements. Process requirements are analyzed to determine useful and practical metrics that can serve as indicators for measuring the process based on the Goal Question Metric (GQM) paradigm [Bas84, Bas88]. The GQM paradigm enforces the principle that measurement is performed for a purpose. GQM is based on a top down approach in which project goals representative of the purpose for the meaFigure 4. Process Design IDEF 0 Diagram Existing Process Design Models I3 Available Process Performers ....

Basili, V. R. and Weiss D. M. "A Methodology for Collecting Valid Software Engineering Data." IEEE Transactions on Software Engineering, 10(6), 1984, 728-738.

....goal oriented, measurement based and bottom updriven. This type of approach uses quantitative evidence in determining where an improvement is needed and, later, whether or not the improvement initiative has been successful. The Plan Do Check Act Cycle, by Shewhart Deming, and the GQM technique [BASI84] can be categorised as analytic models. There are few organisations which create ad hoc measurement programs, just for their own processes ; Prescriptive models. These are closed, staged, assessment based and top down visiondriven. This type of approach uses a formal and prescriptive ....

....the first type of approach, is widely used in the IT field for determining software project measures. It was originally developed to quantitatively define goals and monitor achievements at NASA s Goddard Space Flight Center. This technique, proposed by Victor Basili et al. in the early 80s [BASI84] [BASI85] has since been refined and improved. The basic mechanism, which provides a practical approach for bounding the measurement problem, is the following: for each goal an organisation wants to achieve, a set of related questions must be established, each of which can be answered ....

BASILI V.R. & WEISS D.M., A Methodology for Collecting Valid Software Engineering Data, IEEE Transactions on Software Engineering, Vol. SE-10 No.6, November 1984, IEEE Computer Society, pp. 728-738

....We have followed some suggestions provided by Wholin et al. 34] Perry et al. 28] and Briand et al. 5] on how to perform controlled experiments and have used (with only minor changes) the format proposed by Wholin et al. 34] to describe it. 3.1. Definition. Using the GQM template [1] [2] for goal definition, the experiment goal is defined as follows: Analyse UML class diagram structural complexity metrics For the purpose of Evaluating With respect to their capability of being used as class diagram maintainability indicators From the point of view of Information systems ....

V. Basili and D. Weiss, "A Methodology for Collecting Valid Software Engineering Data", IEEE Transactions on Software Engineering, 10, (1984), pp. 728-738.

....the code and process from the point of view of customer concerns. 3.2 Quantitative Results In this section we present results from several quantitative analyses of the archival data from the Apache project. The measures we derive from these data are well suited to address our research questions [4]; however, they may be unfamiliar to many readers since they are not software metrics that are in wide use, e.g. 6, 10] For this reason, we provide data from several commercial projects, to give the reader some sense of what kinds of results might be expected. Although we picked several ....

BASILI V. R., AND WEISS, D.M. 1984. A methodology for collecting valid software engineering data. IEEE Transactions on Software Engineering, vol. 10, 728-738.

....projects. The main question that is addressed in this paper is: Which factors enable learning in software projects and to what extent 1. Introduction In 1994, software measurement was introduced in the R D department of Schlumberger RPS, by applying the Goal Question Metric (GQM) approach [6] [17] One of the RPS experiences is that the interpretation process of measurements is most important [12] During the interpretation of measurement data, the project team analyses and evaluates their day today processes. Those interpretations are done in structured meetings called feedback ....

Basili, V.R., Weiss, D.M., "A methodology for collecting valid software engineering data", IEEE Transactions on Software Engineering, SE-10 (6), November 1984, pp. 728 - 738.

.... analysis phase should be an explicit definition of the quality factors that are relevant to the product [8] To evaluate the existence or lack of quality in our product, we then need to find suitable metrics for the given quality factors; a useful aid for this purpose is the GQM approach [2]. Finding an appropriate set of metrics for measuring a certain quality factor of the product is often a difficult task. Usability is certainly very tightly coupled with the needs of the intended users, and thus can hardly be evaluated without a set of usability tests taken by a representative ....

V.R. Basili and D. Weiss, "A methodology for collecting valid software engineering data", IEEE Transactions on Software Engineering 10, 16, 1984, pp. 728-738.

....to allow tagging of different versions of the code and to help large teams of developers to work on the same code base. Change data are collected as a side effect when version control systems keep track of versions. Other methods of collecting information make use of surveys or experiments [13] [14]. The resulting information is valuable and detailed, but limited in scope and time, even if the data gathering is not prohibitively expensive. Further, the very act of performing the experiment can alter developers behavior and thereby bias the results. SoftChange is not susceptible to these ....

V. R. Basili and D. M. Weiss, "A methodology for collecting valid software engineering data," IEEE Transactions on Software Engineering, vol. 10, no. 6, pp. 728-737, 1984.

....that all measurement activities be carried out in the context of a well defined measurement goal. In turn, the measurement goal should be clearly connected with an industrial goal, so the measurement program responds to a software organization s needs. The Goal Question Metric (GQM) paradigm [3, 4] provides a framework for deriving measures from measurement goals. The idea is to define a measurement goal, with five dimensions, as follows: Object of Study: the entity or set of entities that should be studied, e.g. a software specification, or a testing process; Purpose: the ....

V. R. Basili and D. Weiss, A Methodology for Collecting Valid Software Engineering Data," IEEE Trans. Software Eng. 10 (1984) 728-738.

....to incompatible data formats exchanges or the use of different tool versions. All these dimensions have also a strong impact on product and process quality. Thus, the quality frameworks and improvement strategies [WCR97] need to be adapted to distributed contexts. The Goal Question Metric (GQM) [BR88, BW88], represents a structured and systematic approach for data collection, based upon the specific needs of the project and the organization. Furthermore, as reported in [BCR94] It can be used in isolation or, better, within the context of a more general approach to software process improvement. ....

V.R. Basili, D.M. Weiss, "A Methodology for Collecting Valid Software Engineering Data", IEEE Transaction on Software Engineering, Vol. 10 no. 6, 1984, pp. 728-738

....cannot be described and analyzed in a fully formal way. We expect GQM MEDEA to be refined and tailored to fit the needs of different application contexts, as we gain experience in applying it. Our framework takes advantage of several research contributions of the literature. Basili and Weiss [5] [6] have provided templates for defining operational experimental goals for software measurement. Our proposal can be seen as an extension of the GQM paradigm [2] 5] 6] which provides a mechanism for generating models, the most challenging part of the paradigm. Melton et al. have studied product ....

....in applying it. Our framework takes advantage of several research contributions of the literature. Basili and Weiss [5] 6] have provided templates for defining operational experimental goals for software measurement. Our proposal can be seen as an extension of the GQM paradigm [2] 5] [6], which provides a mechanism for generating models, the most challenging part of the paradigm. Melton et al. have studied product abstraction properties [33] Weyuker [42] and Tian and Zelkowitz [40] have studied desirable properties for complexity measures. Fenton and Melton [25] and Zuse [43] ....

[Article contains additional citation context not shown here]

V.R. Basili and D. Weiss, "A Methodology for Collecting Valid Software Engineering Data," IEEE Trans. Software Eng., vol. 10, no. 11, pp. 758-773, Nov. 1984.

.... endeavors, we do not think there are absolute criteria for assessing domains, i.e. our criteria will be dependent on the goals of the organization that is creating the product line [14] We therefore apply a goal oriented assessment process based on the goal question metric paradigm (GQM) 3] [4]. A major difference between our approach and other approaches to analyzing product line potential (such as [13] 16] is that we are aiming at a relative comparison of product line domains that is driven by the goals of the organization. It is therefore complementary to product line scoping ....

....us to put a set of candidate domains in order and to make a decision about which domains to develop first. 3. Measurement Program for Domain Assessment Knowing our goals allows us to apply the goalquestion metric (GQM) paradigm to determine assessment measures for candidate domains [3] [4]. We therefore decompose the goals in a top down manner into domain selection criteria (Section 3.1) and sub criteria (Section 3.2) and define a set of interview questions to answer these criteria (Section 3.3) Figure 2, Decomposition) We then gather the data that we need in order to assess our ....

Basili, V., Weiss, D., "A Methodology For Collecting Valid Software Engineering Data", IEEE Trans. on Software Engineering, November, 1984

No context found.

Basili, V.R., Weiss, D., "A Methodology for Collecting Valid Software Engineering Data," IEEE Transactions on Software Engineering, Vol. 10, No. 3, November 1984, pp. 728-738.

No context found.

V. Basili and D. Weiss. A methodology for collecting valid software engineering data, IEEE Transactions on Software Engineering, SE-10 (6) (1984) 728-738.

No context found.

V. Basili, D. Weiss, A Methodology for Collecting Valid Software Engineering Data, in: IEEE Transactions on Software Engineering, SE-10(6):728-738, 1984.

No context found.

Basili, V. and D. Weiss (1984, November). A methodology for collecting valid software engineering data. IEEE Transactions on Software Engineering 10 (6), 728--738.

No context found.

Basili, V. and D. Weiss (1984): A methodology for collecting valid software engineering data. IEEE Transactions on Software Engineering October.

No context found.

V. R. Basili and D. M. Weiss, A Methodology for Collecting Valid Software Engineering Data, IEEE Transactions on Software Engineering, 10(6), pp. 728738, 1984.

No context found.

Victor R. Basili and David M. Weiss. A methodology for collecting valid software engineering data. IEEE Transactions on Software Engineering, SE-10(11):728--738, November 1984.

No context found.

Basili, V.R. and Weiss, D., "A methodology for Collecting Valid Software Engineering Data", IEEE Transactions on Software Engineering, 1984, SE-10. No. 6. pp. 728-738

No context found.

Basili V. and Weiss D. (1984). A Methodology for Collecting Valid Software Engineering Data, IEEE Transactions on Software Engineering, 10, 728-738.

No context found.

V.R. Basili and D. Weiss. A methodology for Collecting Valid Software Engineering Data. IEEE Transactions on Software Engineering. SE-10. No. 6. 728-738, 1984

No context found.

V. Basili and D. Weiss. A methodology for collecting valid software engineering data. IEEE Transactions on Software Engineering, 10(6):728--738, 1984.

No context found.

Basili, V. R., Weiss, D. M., A methodology for collecting valid software engineering data, IEEE Transactions on Software Engineering, Vol. 10, No. 6, 1984.

First 50 documents  Next 50

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC