%0 Journal Article %1 lindvall98 %A Lindvall, Mikael %A Sandahl, Kristian %C New York, NY, USA %D 1998 %I Elsevier Science Inc. %J J. Syst. Softw. %K maintenance evolution software %N 1 %P 19--27 %R 10.1016/S0164-1212(98)10019-5 %T How well do experienced software developers predict software change? %U http://dx.doi.org/10.1016/S0164-1212(98)10019-5 %V 43 %X Project planning is in general a hard problem, and there are many witnesses among software practitioners and their customers who are familiar with the effects of large deviations between planned time for delivery and the actual one. Time is often estimated based on the size of the software to build and it is therefore interesting to investigate how well experienced software developers predict change. Requirements-driven impact analysis (RDIA) identifies the set of software entities needed to be changed to implement a new requirement (defined but previously not implemented) in an existing system. RDIA thus involves a transition from requirements to software entities or to a representative model of the implemented system. RDIA is performed during the release planning phase. Input is a set of requirements and the existing system. Output is, for each requirement, a set of software entities that have to be changed. The output is used as input to many project-planning activities, for example cost estimation based on change volume. The goal of this paper is to quantify how well experienced software developers predict change by conducting RDIA. The means has been an empirical study of RDIA in the industrial object-oriented PMR-project. RDIA has been carried out in two releases, R4 and R6, of this project as a normal part of project developers’ work. This in-depth casestudy has been carried out over four years and in close contact with project developers. Problems with underprediction have been identified—many more classes than predicted are changed. We have also found that project developers are unaware of their own positive and negative capabilities in predicting change. Techniques and methods for data collection and data analysis are provided. Simple and robust methods and tools such as SCCS, Cohen’s kappa, median tests and graphical techniques facilitate future replications in other projects than PMR.

@article{lindvall98, abstract = {Project planning is in general a hard problem, and there are many witnesses among software practitioners and their customers who are familiar with the effects of large deviations between planned time for delivery and the actual one. Time is often estimated based on the size of the software to build and it is therefore interesting to investigate how well experienced software developers predict change. Requirements-driven impact analysis (RDIA) identifies the set of software entities needed to be changed to implement a new requirement (defined but previously not implemented) in an existing system. RDIA thus involves a transition from requirements to software entities or to a representative model of the implemented system. RDIA is performed during the release planning phase. Input is a set of requirements and the existing system. Output is, for each requirement, a set of software entities that have to be changed. The output is used as input to many project-planning activities, for example cost estimation based on change volume. The goal of this paper is to quantify how well experienced software developers predict change by conducting RDIA. The means has been an empirical study of RDIA in the industrial object-oriented PMR-project. RDIA has been carried out in two releases, R4 and R6, of this project as a normal part of project developers’ work. This in-depth casestudy has been carried out over four years and in close contact with project developers. Problems with underprediction have been identified—many more classes than predicted are changed. We have also found that project developers are unaware of their own positive and negative capabilities in predicting change. Techniques and methods for data collection and data analysis are provided. Simple and robust methods and tools such as SCCS, Cohen’s kappa, median tests and graphical techniques facilitate future replications in other projects than PMR.}, added-at = {2006-09-18T06:26:07.000+0200}, address = {New York, NY, USA}, author = {Lindvall, Mikael and Sandahl, Kristian}, biburl = {https://www.bibsonomy.org/bibtex/28f2d5207163947b0507e7ab7d657c2f7/neilernst}, citeulike-article-id = {611046}, description = {Not previously uploaded}, doi = {10.1016/S0164-1212(98)10019-5}, interhash = {fa17f278ad29a4d17e5adbd66ae380c8}, intrahash = {8f2d5207163947b0507e7ab7d657c2f7}, issn = {0164-1212}, journal = {J. Syst. Softw.}, keywords = {maintenance evolution software}, month = {October}, number = 1, pages = {19--27}, priority = {0}, publisher = {Elsevier Science Inc.}, timestamp = {2006-09-18T06:26:07.000+0200}, title = {How well do experienced software developers predict software change?}, url = {http://dx.doi.org/10.1016/S0164-1212(98)10019-5}, volume = 43, year = 1998 }