Learning from project history

Abstract

The lack of lightweight communication channels and other technical and sociological difficulties make it hard for new members of a non-collocated software development team to learn effectively from their more experienced colleagues while they are coming upto-speed on a project. To address this situation, we have developed a tool, named Hipikat, that provides developers with efficient and effective access to the group memory for a software development project that is implicitly formed by all of the artifacts produced during the development. This project memory is built automatically with little or no change to existing work practices. We report an exploratory case study evaluating whether software developers who are new to a project can benefit from the artifacts that Hipikat recommends from the project memory. To assess the appropriateness of the recommendations, we investigated when and how developers queried the project memory, how they evaluated the recommended artifacts, and the process by which they utilized the artifacts. We found that newcomers did use the recommendations and their final solutions exploited the recommended artifacts, although most of the Hipikat queries came in the early stages of a change task. We describe the case study, present qualitative observations, and suggest implications of using project memory as a learning aid for project newcomers.

Knowledge reuse in software projects: Retrieving software development Q&A posts based on project task similarity

“…Regarding the use of historical data, this subject has been explored by some authors to find and recommend artifacts [21]. The work of ČubraniĆ et al [22,23] uses artifacts created within the project, which is similar to what is proposed in this paper, and records what is called "project memory." The "project memory" is a logical association of artifacts, providing developers with a source of associations of recurrent use of artifacts.…”

“…To complement the automated search query, we have conducted a manual search to find articles that were not retrieved by the search query. Our search string did not locate one important source, namely the paper from ČubraniĆ et al [22]. Since this paper is well-…”

“…ČubraniĆ , Davor, Gail C. Murphy, Janice Singer, and Kellogg S. Booth [22] Learning from project history: a case study for software development (Hipikat)…”

When Collective Knowledge Meets Crowd Knowledge in a Smart City: A Prediction Method Combining Open Data Keyword Analysis and Case-Based Reasoning

“…Crowd knowledge has been utilized in past studies of recommendation systems [14, 15] and Q&A systems [16, 17] to resolve the cold-start problem. Crowd knowledge mitigates the cold-start problem since it is not limited by previous knowledge, models, or cases.…”

“…Crowd knowledge can be less biased than knowledge collected from a relatively small set of data sources, such as in studies using case-based reasoning. Because of the lower bias, crowd knowledge has been used in studies of recommendation systems [14, 15] and Q&A systems [16, 17], in which data are collected inexpensively and connectivity is high. Crowd knowledge can be unreliable because participants who provide data have no obligation or responsibility to affirm that the knowledge is correct.…”