Evaluating a model of software managers' information needs: an experiment

Background: The identification of alternative technologies - which is one step of the formal decision-making process -- results in a judgment on whether a technology is considered as a candidate for the further decision-making process. A model has been proposed that aims at improving the delivery of relevant information from ESE research to software managers. Objectives: Evaluate the effectiveness of the model of software managers information needs Method: Experiment with software managers from industry who read two versions of a report on a controlled experiment, one of which contained the information as required by the information needs model. Results: Participants reading the version of the report based on the model perceived that they could judge better whether the technology is appropriate than those reading the original version of the paper. Conclusion: The information needs model provides a means for supporting the identification of alternative solutions and thus has the potential to solve the problem of making decisions.

[1]  Forrest Shull,et al.  Impact of research on practice in the field of inspections, reviews and walkthroughs: learning from successful industrial uses , 2008, SOEN.

[2]  Pearl Brereton,et al.  An investigation of software engineering curricula , 2005, J. Syst. Softw..

[3]  Dietmar Pfahl,et al.  Reporting Experiments in Software Engineering , 2008, Guide to Advanced Empirical Software Engineering.

[4]  J. Bortz,et al.  Forschungsmethoden und Evaluation für Human- und Sozialwissenschaftler , 2006 .

[5]  Gary G. Koch,et al.  Categorical Data Analysis Using The SAS1 System , 1995 .

[6]  Stefan Biffl,et al.  Software Reviews: The State of the Practice , 2003, IEEE Softw..

[7]  Shari Lawrence Pfleeger,et al.  Understanding and improving technology transfer in software engineering , 1999, J. Syst. Softw..

[8]  Marcus Ciolkowski,et al.  What do we know about perspective-based reading? An approach for quantitative aggregation in software engineering , 2009, 2009 3rd International Symposium on Empirical Software Engineering and Measurement.

[9]  Marcus Ciolkowski,et al.  Relevant Information Sources for Successful Technology Transfer: A Survey Using Inspections as an Example , 2007, ESEM 2007.

[10]  G. Glass,et al.  Consequences of Failure to Meet Assumptions Underlying the Fixed Effects Analyses of Variance and Covariance , 1972 .

[11]  Robert L. Glass,et al.  Matching methodology to problem domain , 2004, CACM.

[12]  J. V. Bradley.  Distribution-Free Statistical Tests , 1968 .

[13]  Marcus Ciolkowski.  What do we know about perspective-based reading? An approach for quantitative aggregation in software engineering , 2009, ESEM 2009.

[14]  Gary G. Koch,et al.  Categorical data analysis using the sas® system, 2nd edition , 2000 .

[15]  D. Moher,et al.  The Revised CONSORT Statement for Reporting Randomized Trials: Explanation and Elaboration , 2001, Annals of Internal Medicine.

[16]  Sira Vegas Hernandez.  Characterisation schema for selecting software testing techniques , 2011 .

[17]  Vassiliy Simchera,et al.  Social Statistics , 2011, International Encyclopedia of Statistical Science.

[18]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[19]  Timothy Lethbridge,et al.  What knowledge is important to a software professional? , 2000, Computer.

[20]  Claes Wohlin,et al.  Experimentation in software engineering: an introduction , 2000 .

[21]  Donald J. Reifer.  Is the Software Engineering State of the Practice Getting Closer to the State of the Art? , 2003, IEEE Softw..

[22]  Tore Dybå,et al.  Evidence-based software engineering , 2004, Proceedings. 26th International Conference on Software Engineering.

[23]  Andreas Jedlitschka,et al.  An empirical model of software managers' information needs for software engineering technology selection: a framework to support experimentally-based software engineering technology selection , 2009 .

[24]  Natalia Juristo Juzgado,et al.  Functional Testing, Structural Testing, and Code Reading: What Fault Type Do They Each Detect? , 2003, ESERNET.

[25]  Wanda J. Orlikowski,et al.  The Problem of Statistical Power in MIS Research , 1989, MIS Q..

[26]  L. Cronbach.  Coefficient alpha and the internal structure of tests , 1951 .

[27]  Tore Dybå.  Enabling Software Process Improvement: An Investigation of the Importance of Organizational Issues , 2004, Empirical Software Engineering.

[28]  Austen Rainer,et al.  Persuading developers to "buy into" software process improvement: a local opinion and empirical evidence , 2003, 2003 International Symposium on Empirical Software Engineering, 2003. ISESE 2003. Proceedings..