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Change Impact using Dynamic History Analysis: (Abstract Only)

Published: 21 February 2018 Publication History

Abstract

As the complexity of software systems grows, it becomes increasingly difficult for developers to be aware of all the dependencies that exist between a system's artifacts (e.g., its files or methods). Change impact analysis has been proposed as a technique to overcome this problem, as it suggests to a developer relevant source-code artifacts related to his/her changes. Association rule mining has shown promise for determining change impact by uncovering relevant patterns in a system's change history.
State-of-the-art change impact mining typically makes use of a history of tens of thousands of transactions. This makes a priori generation of all possible rules costly and thus led to the introduction of targeted association rule mining, which only generates rules for transactions relevant to a particular query. Because the set of relevant transactions is much smaller than the complete history, these algorithms are more efficient. However, they still require processing the history's complete set of relevant transactions.
Our work considers the dynamic selection of relevant transactions. It can be viewed as a further constrained version of targeted association rule mining, in which as few as a single relevant transaction might be considered when determining change impact. This initial look at dynamic algorithms empirically studies seven algorithm families. These are referred to as families because some are parameterized and thus give rise to multiple algorithms. Using over 20,000 queries from 19 systems, we empirically show that dynamic algorithms are viable, can be just as applicable as start-of-the-art algorithms, and even outperform them for certain queries.

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cover image ACM Conferences
SIGCSE '18: Proceedings of the 49th ACM Technical Symposium on Computer Science Education
February 2018
1174 pages
ISBN:9781450351034
DOI:10.1145/3159450
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 February 2018

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Author Tags

  1. algorithms
  2. change recommendation
  3. evolutionary coupling
  4. historical co-change
  5. history
  6. software change impact analysis
  7. targeted association rule mining

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SIGCSE '18
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SIGCSE '18 Paper Acceptance Rate 161 of 459 submissions, 35%;
Overall Acceptance Rate 1,787 of 5,146 submissions, 35%

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