<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Debugging is a very time consuming task which is not well supported by existing tools. The existing methods do not provide tools enabling optimal developers’ productivity when debugging regressions in complex systems. In this paper we describe a possible solution aiding differential debugging. The differential debugging technique performs analysis of the regressed system and identifying the cause of the unexpected behavior by comparing to a previous version of the same system. The prototype, idd, inspects two versions of the executable – a baseline and a regressed version. The interactive debugging session runs side by side both executables and allows to examine and to compare various internal states. The architecture can work with multiple information sources comparing data from different tools. We also show how idd can detect performance regressions using information from third-party performance facilities. We illustrate how in practice we can quickly discover regressions in large systems such as the clang compiler.</p></abstract>

Debugging is a very time consuming task which is not well supported by existing tools. The existing methods do not provide tools enabling optimal developers’ productivity when debugging regressions in complex systems. In this paper we describe a possible solution aiding differential debugging. The differential debugging technique performs analysis of the regressed system and identifying the cause of the unexpected behavior by comparing to a previous version of the same system. The prototype, idd, inspects two versions of the executable – a baseline and a regressed version. The interactive debugging session runs side by side both executables and allows to examine and to compare various internal states. The architecture can work with multiple information sources comparing data from different tools. We also show how idd can detect performance regressions using information from third-party performance facilities. We illustrate how in practice we can quickly discover regressions in large systems such as the clang compiler.

IDD – A Platform Enabling Differential Debugging

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"IDD \u2013 A Platform Enabling Differential Debugging"}

Debugging is a very time consuming task which is not well supported by existing tools. The existing methods do not provide tools enabling optimal developers’...

IDD – A Platform Enabling Differential Debugging

Publié en ligne: 27 mars 2020

Pages: 53 - 67

Reçu: 24 nov. 2019

Accepté: 17 janv. 2020

DOI: https://doi.org/10.2478/cait-2020-0004

Mots clés
IDD, differential debugging, functional regressions, performance regressions, complex systems, side by side debugging, interactive visual debugging

© 2020 Martin Vassilev et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

IDD – A Platform Enabling Differential Debugging

Publié en ligne: 27 mars 2020

Pages: 53 - 67

Reçu: 24 nov. 2019

Accepté: 17 janv. 2020

DOI: https://doi.org/10.2478/cait-2020-0004

Mots clésIDD, differential debugging, functional regressions, performance regressions, complex systems, side by side debugging, interactive visual debugging

© 2020 Martin Vassilev et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
IDD, differential debugging, functional regressions, performance regressions, complex systems, side by side debugging, interactive visual debugging