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Information and Software Technology

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Example publications from this periodical

The following articles are from "Information and Software Technology":

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Volume 38
Issue 12

Cerpa, Narciso and Verner, June M. (1996): Prototyping: Some New Results. In Information and Software Technology, 38 (12) pp. 743-755.

Volume 39
Issue 39

Chan, H., Wei, K. and Siau, Keng (1997): A System for Query Comprehension. In Information and Software Technology, 39 (39) pp. 141-148.

Volume 41
Issue 2

Walczak, Steven and Cerpa, Narciso (1999): Heuristic Principles for the Design of Artificial Neural Networks. In Information and Software Technology, 41 (2) pp. 107-117.

Artificial neural networks were used to support applications across a variety of business and scientific disciplines during the past years. Artificial neural network applications are frequently viewed as black boxes which mystically determine complex patterns in data. Contrary to this popular view, neural network designers typically perform extensive knowledge engineering and incorporate a significant amount of domain knowledge into artificial neural networks. This paper details heuristics that utilize domain knowledge to produce an artificial neural network with optimal output performance. The effect of using the heuristics on neural network performance is illustrated by examining several applied artificial neural network systems. Identification of an optimal performance artificial neural network requires that a full factorial design with respect to the quantity of input nodes, hidden nodes, hidden layers, and learning algorithm be performed. The heuristic methods discussed in this paper produce optimal or near-optimal performance artificial neural networks using only a fraction of the time needed for a full factorial design.

© All rights reserved Walczak and Cerpa and/or Elsevier

Volume 49
Issue 5

Siau, Keng and Wang, Y. (2007): Cognitive Evaluation of Information Modeling Methods. In Information and Software Technology, 49 (5) pp. 455-474.

Issue 2

Verner, June M., Evanco, William M. and Cerpa, Narciso (2007): State of the practice: how important is effort estimation to software development success?. In Information and Software Technology, 49 (2) pp. 181-193.

During discussions with a group of U.S. software developers we explored the effect of schedule estimation practices and their implications for software project success. Our objective is not only to explore the direct effects of cost and schedule estimation on the perceived success or failure of a software development project, but also to quantitatively examine a host of factors surrounding the estimation issue that may impinge on project outcomes. We later asked our initial group of practitioners to respond to a questionnaire that covered some important cost and schedule estimation topics. Then, in order to determine if the results are generalizable, two other groups from the US and Australia, completed the questionnaire. Based on these convenience samples, we conducted exploratory statistical analyses to identify determinants of project success and used logistic regression to predict project success for the entire sample, as well as for each of the groups separately. From the developer point of view, our overall results suggest that success is more likely if the project manager is involved in schedule negotiations, adequate requirements information is available when the estimates are made, initial effort estimates are good, take staff leave into account, and staff are not added late to meet an aggressive schedule. For these organizations we found that developer input to the estimates did not improve the chances of project success or improve the estimates. We then used the logistic regression results from each single group to predict project success for the other two remaining groups combined. The results show that there is a reasonable degree of generalizability among the different groups.

© All rights reserved Verner et al. and/or Elsevier

Volume 52
Issue 9

Cerpa, Narciso, Bardeen, Matthew, Kitchenham, Barbara and Verner, June (2010): Evaluating Logistic Regression Models to Estimate Software Project Outcomes. In Information and Software Technology, 52 (9) pp. 934-944.

Context: Software has been developed since the 1960s but the success rate of software development projects is still low. During the development of software, the probability of success is affected by various practices or aspects. To date, it is not clear which of these aspects are more important in influencing project outcome. Objective: In this research, we identify aspects which could influence project success, build prediction models based on the aspects using data collected from multiple companies, and then test their performance on data from a single organization. Method: A survey-based empirical investigation was used to examine variables and factors that contribute to project outcome. Variables that were highly correlated to project success were selected and the set of variables was reduced to three factors by using principal components analysis. A logistic regression model was built for both the set of variables and the set of factors, using heterogeneous data collected from two different countries and a variety of organizations. We tested these models by using a homogeneous hold-out dataset from one organization. We used the receiver operating characteristic (ROC) analysis to compare the performance of the variable and factor-based models when applied to the homogeneous dataset. Results: We found that using raw variables or factors in the logistic regression models did not make any significant difference in predictive capability. The prediction accuracy of these models is more balanced when the cut-off is set to the ratio of success to failures in the datasets used to build the models. We found that the raw variable and factor-based models predict significantly better than random chance. Conclusion: We conclude that an organization wishing to estimate whether a project will succeed or fail may use a model created from heterogeneous data derived from multiple organizations.

© All rights reserved Cerpa et al. and/or Elsevier


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Jul 11

Creative without strategy is called ‘art‘. Creative with strategy is called ‘advertising‘

-- Jef I. Richards


Help us help you!