Towards understanding the rhetoric of small changes

Abstract

Understanding the impact of software changes has been a challenge since software systems were first developed. With the increasing size and complexity of systems, this problem has become more difficult. There are many ways to identify change impact from the plethora of software artifacts produced during development and maintenance. We present the analysis of the software development process using change and defect history data. Specifically, we address the problem of small changes. The studies revealed that (1) there is less than 4 percent probability that a one-line change will introduce an error in the code; (2) nearly 10 percent of all changes made during the maintenance of the software under consideration were one-line changes; (3 the phenomena of change differs for additions, deletions and modifications as well as for the number of lines affected.

A survey and taxonomy of approaches for mining software repositories in the context of software evolution

“…The focus of a study presented by Purushothaman and Perry [67,68] was to understand the impact of small changes, particularly one-line changes, with regards to faults, the relationship between different types of changes (i.e., add, delete, and modify), the reason for the change (i.e., corrective, adaptive, and perfective), and dependencies between changes. A change was considered to be a one-line change if there was at least one modification to a single line, at least one line was replaced by a single line (i.e., multiple lines deleted followed by an addition of a single line), a new statement was added between existing lines, or a single line was deleted.…”

A Survey on Mining Software Repositories

“…Decision tree has been used to predict developers' contribution in [145]. In [65], SVM has been applied for the bug triage and in [147], association rule mining has refactoring [72], [73] API-change [74], [75], [77], [80], [81] change patterns [83]- [88], [90], [160] team-activity developer's contribution [55], [91], [93], [94], [154] experties of developers [96], [97], [149] tool support [98], [99], [128], [151] helpful information [100] comprehension visualization [101], [102], [156] identifiers [104], [105], [153] recording operations [106] validation metrics [45], [107], [157] tool [108] clones [109]- [112], [150], [159] bug [113], [114] development& evolution development [118]- [120] evolution …”

“…Purushothaman and Perry analyzed the impact of small changes, especially oneline changes, about faults, relations between changes (add, delete, and modify), reasons of changes (corrective, adaptive, and perfective), and dependencies between changes [83], [84]. They derived some empirical results as follows: About 10% changes were one-line changes; 50% changes were at most about 10 loc (line of codes) changes; 95% of changes were 50 loc changes; Most changes were 'adaptive' and related to 'code addition'.…”

Automatic mining of source code repositories to improve bug finding techniques

“…Purushothaman and Perry present a study of small changes to determine the impact they have on the software [23]. Specifically, they look at the properties of the change itself, number of lines added, removed, or modified, rather than properties of the code being changed, to determine how small changes affect the code.…”