Mushtaq Raza

PSP PAIR: Automated Personal Software Process Performance Analysis and Improvement Recommendation

High-maturity software development processes, making intensive use of metrics and quantitative methods, such as the Personal Software Process (PSP) and the Team Software Process (TSP), can generate a significant amount of data that can be periodically analyzed to identify performance problems, determine their root causes and devise improvement actions. Currently, there are several tools that automate data collection and produce performance charts for manual analysis in the context of the PSP/TSP, but practically no tool support exists for automating the data analysis and the recommendation of improvement actions. Manual analysis of this performance data is problematic because of the large amount of data to analyze and the time and expertise required. Hence, we propose in this paper a performance model and a tool (named PSP PAIR) to automate the analysis of performance data produced in the context of the PSP, namely, identify performance problems and their root causes, and recommend improvement actions. The work presented is limited to the analysis of the time estimation performance of PSP developers, but is extensible to other performance indicators and development processes.

ProcessPAIR: a tool for automated performance analysis and improvement recommendation in software development

High-maturity software development processes can generate significant amounts of data that can be periodically analyzed to identify performance problems, determine their root causes and devise improvement actions. However, conducting that analysis manually is challenging because of the potentially large amount of data to analyze and the effort and expertise required. In this paper, we present ProcessPAIR, a novel tool designed to help developers analyze their performance data with less effort, by automatically identifying and ranking performance problems and potential root causes, so that subsequent manual analysis for the identification of deeper causes and improvement actions can be properly focused. The analysis is based on performance models defined manually by process experts and calibrated automatically from the performance data of many developers. We also show how ProcessPAIR was successfully applied for the Personal Software Process (PSP). A video about ProcessPAIR is available in https://youtu.be/dEk3fhhkduo.

A model for analyzing performance problems and root causes in the personal software process

High‐maturity software development processes, such as the Team Software Process and the accompanying Personal Software Process (PSP), can generate significant amounts of data that can be periodically analyzed to identify performance problems, determine their root causes, and devise improvement actions. However, there is a lack of tool support for automating that type of analysis, and hence diminish the manual effort and expert knowledge required. So, we propose in this paper a comprehensive performance model, addressing time estimation accuracy, quality, and productivity, to enable the automated (tool based) analysis of performance data produced by PSP developers, namely, identify and rank performance problems and their root causes. A PSP data set referring to more than 30 000 projects was used to validate and calibrate the model. Copyright

A model for analyzing estimation, productivity, and quality performance in the personal software process

High-maturity software development processes, making intensive use of metrics and quantitative methods, such as the Team Software Process (TSP) and the accompanying Personal Software Process (PSP), can generate a significant amount of data that can be periodically analyzed to identify performance problems, determine their root causes and devise improvement actions. However, there is a lack of tool support for automating the data analysis and the recommendation of improvement actions, and hence diminish the manual effort and expert knowledge required. So, we propose in this paper a comprehensive performance model, addressing time estimation accuracy, quality and productivity, to enable the automated (tool based) analysis of performance data produced in the context of the PSP, namely, identify performance problems and their root causes, and subsequently recommend improvement actions. Performance ranges and dependencies in the model were calibrated and validated, respectively, based on a large PSP data set referring to more than 30,000 finished projects.

FACTORS AFFECTING PERSONAL SOFTWARE DEVELOPMENT PRODUCTIVITY: A CASE STUDY WITH PSP DATA

Understanding the factors that affect the productivity of software developers and may cause productivity variations among individuals and projects is important for anyone interested in improving software engineering performance and estimates, and in particular for users of high-maturity processes, such as the Personal Software Process (PSP) and the Team Software Process (TSP). In order to contribute to the understanding of the personal and non-personal factors that affect productivity, we analyzed the data from more than 3000 developers that concluded successfully the 10 projects of the PSP for Engineers I/II training course. Regarding non-personal factors, by conducting a detailed per-phase analysis, we found significant variations of productivity among projects that can be partially explained by process changes. Regarding personal factors, we found significant variations among individuals that can be partially explained by personal experience.

Empirical Evaluation of the ProcessPAIR Tool for Automated Performance Analysis

Software development processes can generate significant amounts of data that can be periodically analyzed to identify performance problems, determine their root causes and devise improvement actions. However, conducting that analysis manually is challenging because of the potentially large amount of data to analyze and the effort and expertise required. ProcessPAIR is a novel tool designed to help developers analyze their performance data with less effort, by automatically identifying and ranking performance problems and potential root causes. The analysis is based on performance models derived from the performance data of a large community of developers. In this paper, we present the results of an experiment conducted in the context of Personal Software Process (PSP) training, to show that ProcessPAIR is able to accurately identify and rank performance problems and potential root causes of individual developers so that subsequent manual analysis for the identification of deeper causes and improvement actions can be properly focused.

WebProcessPAIR: recommendation system for software process improvement

ProcessPAIR is a novel tool for helping software developers analyzing their personal performance. Based on a performance model calibrated from the anonymized performance data of many developers and the performance data submitted by an individual developer, it automatically identifies and ranks potential performance problems and their root causes for that developer. In this work we present WebProcessPAIR, which extends ProcessPAIR with the ability to recommend improvement actions to address the root causes identified, based on a crowdsourcing approach. A case study illustrates WebProcessPAIR usage.

A Benchmark-Based Approach for Ranking Root Causes of Performance Problems in Software Development

In previous work we proposed a performance analysis model for automatically identifying potential root causes of performance problems in personal software development. In this paper we present an approach for automatically ranking those potential root causes based on a cost-benefit estimate that takes into account historical data. The approach was applied for the Personal Software Process, taking advantage of a large data set referring to more than 30,000 projects, but can be replicated in other contexts.