LFSR generation for high test coverage and low hardware overhead

2019 ◽  
Vol 14 (1) ◽  
pp. 27-36
Author(s):  
Leonel Hernández Martínez ◽  
Saqib Khursheed ◽  
Sudhakar Mannapuram Reddy
Keyword(s):  
Test Coverage ◽  
Hardware Overhead ◽  
High Test

scholarly journals The charming code that error messages are talking about

2015 ◽  
Author(s):  
Joshua C Campbell ◽  
Abram Hindle
Keyword(s):  
Source Code ◽  
Test Coverage ◽  
Random Mutation ◽  
Cyclomatic Complexity ◽  
Software Metric ◽  
Automated Tools ◽  
The One ◽  
Standard Software ◽  
High Test ◽  
Actual Location

The intent of high test coverage is to ensure that the dark nooks and crannies of code are exercised and tested. In a language like Python this is especially important as syntax errors can lurk in unevaluated blocks, only to be discovered once they are finally executed. Bugs that present themselves as error messages mentioning a line of code which is unrelated to the cause of the bug can be difficult and time-consuming to fix when a developer must first determine the actual location of the fault. A new code metric, charm, is presented. Charm can be used by developers, researchers, and automated tools to gain a deeper understanding of source code and become aware of potentially hidden faults, areas of code which are not sufficiently tested, and areas of code which may be more difficult to debug. Charm quantifies the property that error messges caused by a fault at one location don't always reference that location. In fact, error messages seem to prefer to reference some locations far more often than others. The quantity of charm can be estimated by averaging results from a random sample of similar programs to the one being measured by a procedure of random-mutation testing. Charm is estimated for release-quality Python software, requiring many thousands of similar Python programs to be executed. Charm has some correlation with a standard software metric, cyclomatic complexity. 21 code features which may have some relationship with charm and cyclomatic complexity are investigated, of which five are found to be significantly related with charm. These five features are then used to build a linear model which attempts to estimate charm cheaply.

scholarly journals The charming code that error messages are talking about

2015 ◽  
Author(s):  
Joshua C Campbell ◽  
Abram Hindle
Keyword(s):  
Source Code ◽  
Test Coverage ◽  
Random Mutation ◽  
Cyclomatic Complexity ◽  
Software Metric ◽  
Automated Tools ◽  
The One ◽  
Standard Software ◽  
High Test ◽  
Actual Location

The intent of high test coverage is to ensure that the dark nooks and crannies of code are exercised and tested. In a language like Python this is especially important as syntax errors can lurk in unevaluated blocks, only to be discovered once they are finally executed. Bugs that present themselves as error messages mentioning a line of code which is unrelated to the cause of the bug can be difficult and time-consuming to fix when a developer must first determine the actual location of the fault. A new code metric, charm, is presented. Charm can be used by developers, researchers, and automated tools to gain a deeper understanding of source code and become aware of potentially hidden faults, areas of code which are not sufficiently tested, and areas of code which may be more difficult to debug. Charm quantifies the property that error messges caused by a fault at one location don't always reference that location. In fact, error messages seem to prefer to reference some locations far more often than others. The quantity of charm can be estimated by averaging results from a random sample of similar programs to the one being measured by a procedure of random-mutation testing. Charm is estimated for release-quality Python software, requiring many thousands of similar Python programs to be executed. Charm has some correlation with a standard software metric, cyclomatic complexity. 21 code features which may have some relationship with charm and cyclomatic complexity are investigated, of which five are found to be significantly related with charm. These five features are then used to build a linear model which attempts to estimate charm cheaply.

scholarly journals The charming code that error messages are talking about

2015 ◽  
Author(s):  
Joshua C Campbell ◽  
Abram Hindle
Keyword(s):  
Source Code ◽  
Test Coverage ◽  
Random Mutation ◽  
Cyclomatic Complexity ◽  
Software Metric ◽  
Automated Tools ◽  
The One ◽  
Standard Software ◽  
High Test ◽  
Actual Location

The intent of high test coverage is to ensure that the dark nooks and crannies of code are exercised and tested. In a language like Python this is especially important as syntax errors can lurk in unevaluated blocks, only to be discovered once they are finally executed. Bugs that present themselves as error messages mentioning a line of code which is unrelated to the cause of the bug can be difficult and time-consuming to fix when a developer must first determine the actual location of the fault. A new code metric, charm, is presented. Charm can be used by developers, researchers, and automated tools to gain a deeper understanding of source code and become aware of potentially hidden faults, areas of code which are not sufficiently tested, and areas of code which may be more difficult to debug. Charm quantifies the property that error messges caused by a fault at one location don't always reference that location. In fact, error messages seem to prefer to reference some locations far more often than others. The quantity of charm can be estimated by averaging results from a random sample of similar programs to the one being measured by a procedure of random-mutation testing. Charm is estimated for release-quality Python software, requiring many thousands of similar Python programs to be executed. Charm has some correlation with a standard software metric, cyclomatic complexity. 21 code features which may have some relationship with charm and cyclomatic complexity are investigated, of which five are found to be significantly related with charm. These five features are then used to build a linear model which attempts to estimate charm cheaply.

Single-ended SRAM with high test coverage and short test time

2000 ◽  
Vol 35 (1) ◽  
pp. 114-118 ◽  
Author(s):  
Chua-Chin Wang ◽  
Chi-Feng Wu ◽  
Rain-Ted Hwang ◽  
Chia-Hsiung Kao
Keyword(s):  
Test Time ◽  
Test Coverage ◽  
Short Test ◽  
High Test
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