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Virginia Tech

DR_BEV: Developer Recommendation Based on Executed Vocabulary

Abstract

dc:description.abstract

Bug-fixing, or fixing known errors in computer software, makes up a large portion of software development expenses. Once a bug is discovered, it must be assigned to an appropriate developer who has the necessary expertise to fix the bug. This bug-assignment task has traditionally been done manually. However, this manual task is time-consuming, error-prone, and tedious. Therefore, automatic bug assignment techniques have been developed to facilitate this task. Most of the existing techniques are report-based. That is, they work on bugs that are textually described in bug reports. However, only a subset of bugs that are observed as a faulty program execution are also described textually. Certain bugs, such as security vulnerability bugs, are only represented with a faulty program execution, and are not described textually. In other words, these bugs are represented by a code coverage, which indicates which lines of source code have been executed in the faulty program execution. Promptly fixing these software security vulnerability bugs is necessary in order to manage security threats. Accordingly, execution-based bug assignment techniques, which model a bug with a faulty program execution, are an important tool in fixing software security bugs. In this thesis, we compare WhoseFault, an existing execution-based bug assignment technique, to report-based techniques. Additionally, we propose DR_BEV (Developer Recommendation Based on Executed Vocabulary), a novel execution-based technique that models developer expertise based on the vocabulary of each developer's source code contributions, and we demonstrate that this technique out-performs the current state-of-the-art execution-based technique. Our observations indicate that report-based techniques perform better than execution-based techniques, but not by a wide margin. Therefore, while a report-based technique should be used if a report exists for a bug, our results should provide confidence in the scenarios in which only execution-based techniques are applicable.

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Computer Science and Applications
Department dc:contributor.department
Computer Science
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2020

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Bendelac, Alon
Chair dc:contributor.committeechair
  • Servant Cortes, Francisco Javier
Committee members dc:contributor.committeemember
  • Meng, Na
  • Balci, Osman

Subjects

dc:subject × 4

Rights

dc:rights
Statement dc:rights
  • In Copyright

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:25855
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/106700

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Bendelac, Alon. DR_BEV: Developer Recommendation Based on Executed Vocabulary. masters thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/106700