|
Showing 1 - 3 of
3 matches in All Departments
Program debugging has always been a difficult and time-consuming
task in the context of software development, where spectrum-based
fault localization (SBFL) is one of the most widely studied
families of techniques. While it's not particularly difficult to
learn about the process and empirical performance of a particular
SBFL technique from the available literature, researchers and
practitioners aren't always familiar with the underlying theories.
This book provides the first comprehensive guide to fundamental
theories in SBFL, while also addressing some emerging challenges in
this area. The theoretical framework introduced here reveals the
intrinsic relations between various risk evaluation formulas,
making it possible to construct a formula performance hierarchy.
Further extensions of the framework provide a sufficient and
necessary condition for a general maximal formula, as well as
performance comparisons for hybrid SBFL methods. With regard to
emerging challenges in SBFL, the book mainly covers the frequently
encountered oracle problem in SBFL and introduces a metamorphic
slice-based solution. In addition, it discusses the challenge of
multiple-fault localization and presents cutting-edge approaches to
overcoming it. SBFL is a widely studied research area with a
massive amount of publications. Thus, it is essential that the
software engineering community, especially those involved in
program debugging, software maintenance and software quality
assurance (including both newcomers and researchers who want to
gain deeper insights) understand the most fundamental theories -
which could also be very helpful to ensuring the healthy
development of the field.
Program debugging has always been a difficult and time-consuming
task in the context of software development, where spectrum-based
fault localization (SBFL) is one of the most widely studied
families of techniques. While it's not particularly difficult to
learn about the process and empirical performance of a particular
SBFL technique from the available literature, researchers and
practitioners aren't always familiar with the underlying theories.
This book provides the first comprehensive guide to fundamental
theories in SBFL, while also addressing some emerging challenges in
this area. The theoretical framework introduced here reveals the
intrinsic relations between various risk evaluation formulas,
making it possible to construct a formula performance hierarchy.
Further extensions of the framework provide a sufficient and
necessary condition for a general maximal formula, as well as
performance comparisons for hybrid SBFL methods. With regard to
emerging challenges in SBFL, the book mainly covers the frequently
encountered oracle problem in SBFL and introduces a metamorphic
slice-based solution. In addition, it discusses the challenge of
multiple-fault localization and presents cutting-edge approaches to
overcoming it. SBFL is a widely studied research area with a
massive amount of publications. Thus, it is essential that the
software engineering community, especially those involved in
program debugging, software maintenance and software quality
assurance (including both newcomers and researchers who want to
gain deeper insights) understand the most fundamental theories -
which could also be very helpful to ensuring the healthy
development of the field.
With the increasing complexity of and dependency on software,
software products may suffer from low quality, high prices, be hard
to maintain, etc. Software defects usually produce incorrect or
unexpected results and behaviors. Accordingly, software defect
prediction (SDP) is one of the most active research fields in
software engineering and plays an important role in software
quality assurance. Based on the results of SDP analyses, developers
can subsequently conduct defect localization and repair on the
basis of reasonable resource allocation, which helps to reduce
their maintenance costs. This book offers a comprehensive picture
of the current state of SDP research. More specifically, it
introduces a range of machine-learning-based SDP approaches
proposed for different scenarios (i.e., WPDP, CPDP, and HDP). In
addition, the book shares in-depth insights into current SDP
approaches’ performance and lessons learned for future SDP
research efforts. We believe these theoretical analyses and
emerging challenges will be of considerable interest to all
researchers, graduate students, and practitioners who want to gain
deeper insights into and/or find new research directions in SDP. It
offers a comprehensive introduction to the current state of SDP and
detailed descriptions of representative SDP approaches.
|
You may like...
Gloria
Sam Smith
CD
R407
Discovery Miles 4 070
|