An empirical study of system design instability metric and design evolution in an agile software process

Software project tracking and project plan adjustment are two important software engineering activities. The class growth shows the design evolution of the software. The System Design Instability (SDI) metric indicates the progress of an object oriented (OO) project once the project is set in motion. The SDI metric provides information on project evolution to project managers for possible adjustment to the project plan. The objectives of this paper are to test if the System Design Instability metric can be used to estimate and re-plan software projects in an XPlike agile process and study system design evolution in the Agile software process. We present an empirical study of the class growth and the SDI metric in two OO systems, developed using an agile process similar to Extreme Programming (XP). We analyzed the system evolutionary data collected on a daily basis from the two systems. We concluded that: the systems’ class growth follows observable trends, the SDI metric can indicate project progress with certain trends, and the SDI metric is correlated with XP activities. In both of the analyzed systems, we observed two consistent jumps in the SDI metric values in early and late development phases. Part of the results agrees with a previous empirical study in another environmen

An empirical study of relationships among extreme programming engineering activities

Extreme programming (XP) is an agile software process that promotes early and quick production of working code. In this paper, we investigated the relationship among three XP engineering activities: new design, refactoring, and error fix. We found that the more the new design performed to the system the less refactoring and error fix were performed. However, the refactoring and error fix efforts did not seem to be related. We also found that the error fix effort is related to number of days spent on each story, while new design is not. The relationship between the refactoring effort and number of days spent on each story was not conclusive

An Empirical Validation of Object-Oriented Metrics in Two Different Iterative Software Processes

Object-oriented (OO) metrics are used mainly to predict software engineering activities/efforts such as maintenance effort, error proneness, and error rate. There have been discussions about the effectiveness of metrics in different contexts. In this paper, we present an empirical study of OO metrics in two iterative processes: the short-cycled agile process and the long-cycled framework evolution process. We find that OO metrics are effective in predicting design efforts and source lines of code added, changed, and deleted in the short-cycled agile process and ineffective in predicting the same aspects in the long-cycled framework process. This leads us to believe that OO metrics’ predictive capability is limited to the design and implementation changes during the development iterations, not the long-term evolution of an established system in different releases

An Empirical Validation of Object-Oriented Metrics in Two Different Iterative Software Processes

Object-oriented (OO) metrics are used mainly to predict software engineering activities/efforts such as maintenance effort, error proneness, and error rate. There have been discussions about the effectiveness of metrics in different contexts. In this paper, we present an empirical study of OO metrics in two iterative processes: the short-cycled agile process and the long-cycled framework evolution process. We find that OO metrics are effective in predicting design efforts and source lines of code added, changed, and deleted in the short-cycled agile process and ineffective in predicting the same aspects in the long-cycled framework process. This leads us to believe that OO metrics’ predictive capability is limited to the design and implementation changes during the development iterations, not the long-term evolution of an established system in different releases

An empirical study of relationships among extreme programming engineering activities

Extreme programming (XP) is an agile software process that promotes early and quick production of working code. In this paper, we investigated the relationship among three XP engineering activities: new design, refactoring, and error fix. We found that the more the new design performed to the system the less refactoring and error fix were performed. However, the refactoring and error fix efforts did not seem to be related. We also found that the error fix effort is related to number of days spent on each story, while new design is not. The relationship between the refactoring effort and number of days spent on each story was not conclusive

An empirical study of system design instability metric and design evolution in an agile software process

Software project tracking and project plan adjustment are two important software engineering activities. The class growth shows the design evolution of the software. The System Design Instability (SDI) metric indicates the progress of an object oriented (OO) project once the project is set in motion. The SDI metric provides information on project evolution to project managers for possible adjustment to the project plan. The objectives of this paper are to test if the System Design Instability metric can be used to estimate and re-plan software projects in an XPlike agile process and study system design evolution in the Agile software process. We present an empirical study of the class growth and the SDI metric in two OO systems, developed using an agile process similar to Extreme Programming (XP). We analyzed the system evolutionary data collected on a daily basis from the two systems. We concluded that: the systems’ class growth follows observable trends, the SDI metric can indicate project progress with certain trends, and the SDI metric is correlated with XP activities. In both of the analyzed systems, we observed two consistent jumps in the SDI metric values in early and late development phases. Part of the results agrees with a previous empirical study in another environmen