Mesterséges Intelligencia Nemzeti Laboratórium / Artificial Intelligence National
Laboratory(MILAB) Támogató: NKFIH
Tématerületi Kiválósági Program 2021(TKP2021-NVA-09) Támogató: NKFIH
Tool support for automated fault localization in program debugging is limited because
state-of-the-art algorithms often fail to provide efficient help to the user. They
usually offer a ranked list of suspicious code elements, but the fault is not guaranteed
to be found among the highest ranks. In Spectrum-Based Fault Localization (SBFL) –
which uses code coverage information of test cases and their execution outcomes to
calculate the ranks –, the developer has to investigate several locations before finding
the faulty code element. Yet, all the knowledge she a priori has or acquires during
this process is not reused by the SBFL tool. There are existing approaches in which
the developer interacts with the SBFL algorithm by giving feedback on the elements
of the prioritized list. We propose a new approach called iFL which extends interactive
approaches by exploiting contextual knowledge of the user about the next item in the
ranked list (e. g., a statement), with which larger code entities (e. g., a whole
function) can be repositioned in their suspiciousness. We implemented a closely related
algorithm proposed by Gong et al. , called Talk . First, we evaluated iFL using simulated
users, and compared the results to SBFL and Talk . Next, we introduced two types of
imperfections in the simulation: user’s knowledge and confidence levels. On SIR and
Defects4J, results showed notable improvements in fault localization efficiency, even
with strong user imperfections. We then empirically evaluated the effectiveness of
the approach with real users in two sets of experiments: a quantitative evaluation
of the successfulness of using iFL , and a qualitative evaluation of practical uses
of the approach with experienced developers in think-aloud sessions.
