scholarly journals Ending null pointer crashes

2017 ◽  
Vol 60 (5) ◽  
pp. 8-9 ◽  
Author(s):  
Bertrand Meyer
Keyword(s):  
Null Pointer

Static detection of array bounds errors and null pointer dereference in Java

2009 ◽  
Vol 29 (5) ◽  
pp. 1376-1379 ◽  
Author(s):  
Bai-qiang CHEN ◽  
Tao GUO ◽  
Hui RUAN ◽  
Jun YAN
Keyword(s):  
Static Detection ◽  
Null Pointer

scholarly journals On the feasibility of automated prediction of bug and non-bug issues

2020 ◽  
Vol 25 (6) ◽  
pp. 5333-5369 ◽  
Author(s):  
Steffen Herbold ◽  
Alexander Trautsch ◽  
Fabian Trautsch
Keyword(s):  
Development Process ◽  
State Of The Art ◽  
Structural Information ◽  
Past Research ◽  
Prediction Performance ◽  
Use Case ◽  
Bug Reports ◽  
Automated Prediction ◽  
The Difference ◽  
Null Pointer

Abstract Context Issue tracking systems are used to track and describe tasks in the development process, e.g., requested feature improvements or reported bugs. However, past research has shown that the reported issue types often do not match the description of the issue. Objective We want to understand the overall maturity of the state of the art of issue type prediction with the goal to predict if issues are bugs and evaluate if we can improve existing models by incorporating manually specified knowledge about issues. Method We train different models for the title and description of the issue to account for the difference in structure between these fields, e.g., the length. Moreover, we manually detect issues whose description contains a null pointer exception, as these are strong indicators that issues are bugs. Results Our approach performs best overall, but not significantly different from an approach from the literature based on the fastText classifier from Facebook AI Research. The small improvements in prediction performance are due to structural information about the issues we used. We found that using information about the content of issues in form of null pointer exceptions is not useful. We demonstrate the usefulness of issue type prediction through the example of labelling bugfixing commits. Conclusions Issue type prediction can be a useful tool if the use case allows either for a certain amount of missed bug reports or the prediction of too many issues as bug is acceptable.

Practical null pointer dereference detection via value-dependence analysis

2015 ◽  
Author(s):  
Sen Ma ◽  
MingYang Jiao ◽  
ShiKun Zhang ◽  
Wen Zhao ◽  
Dong Wei Wang
Keyword(s):  
Dependence Analysis ◽  
Null Pointer

Predicting null-pointer dereferences in concurrent programs

2012 ◽  
Author(s):  
Azadeh Farzan ◽  
P. Madhusudan ◽  
Niloofar Razavi ◽  
Francesco Sorrentino
Keyword(s):  
Concurrent Programs ◽  
Null Pointer

scholarly journals The Optimization of a Symbolic Execution Engine for Detecting Runtime Errors

2017 ◽  
Vol 23 (2) ◽  
pp. 573-597
Author(s):  
István Kádár
Keyword(s):  
Search Strategy ◽  
Symbolic Execution ◽  
Real Life ◽  
Time Frame ◽  
Basic Block ◽  
Test Execution ◽  
Execution Engine ◽  
Branching Points ◽  
Java Programs ◽  
Null Pointer

In a software system, most of the runtime failures may come to light only during test execution, and this may have a very high cost. To help address this problem, a symbolic execution engine called RTEHunter, which has been developed at the Department of Software Engineering at the University of Szeged, is able to detect runtime errors (such as null pointer dereference, bad array indexing, division by zero) in Java programs without actually running the program in a real-life environment. Applying the theory of symbolic execution, RTEHunter builds a tree, called a symbolic execution tree, composed of all the possible execution paths of the program. RTEHunter detects runtime issues by traversing the symbolic execution tree and if a certain condition is fulfilled the engine reports an issue. However, as the number of execution paths increases exponentially with the number of branching points, the exploration of the whole symbolic execution tree becomes impossible in practice. To overcome this problem, different kinds of constraints can be set up over the tree. E.g. the number of symbolic states, the depth of the execution tree, or the time consumption could be restricted. Our goal in this study is to find the optimal parametrization of RTEHunter in terms of the maximum number of states, maximum depth of the symbolic execution tree and search strategy in order to find more runtime issues in a shorter time. Results on three open-source Java systems demonstrate that more runtime issues can be detected in the 0 to 60 basic block-depth levels than in deeper ones within the same time frame. We also developed two novel search strategies for traversing the tree based on the number of null pointer references in the program and on linear regression that performs better than the default depth-first search strategy.

Evaluating and tuning a static analysis to find null pointer bugs

2006 ◽  
Vol 31 (1) ◽  
pp. 13-19 ◽  
Author(s):  
David Hovemeyer ◽  
Jaime Spacco ◽  
William Pugh
Keyword(s):  
Static Analysis ◽  
Null Pointer

Fault Localization for Null Pointer Exception Based on Stack Trace and Program Slicing

2012 ◽  
Author(s):  
Shujuan Jiang ◽  
Wei Li ◽  
Haiyang Li ◽  
Yanmei Zhang ◽  
Hongchang Zhang ◽  
...  
Keyword(s):  
Fault Localization ◽  
Program Slicing ◽  
Null Pointer
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