func Bootstrap
has a cyclomatic complexity of 57 with "critical" risk121}
122
123// Bootstrap Bootstrap for models
124func (mc *ModelCache) Bootstrap() {125 mc.Lock()
126 defer mc.Unlock()
127 if mc.done {
func ReadValues
has a cyclomatic complexity of 31 with "very-high" risk1892}
1893
1894// ReadValues query sql, read values , save to *[]ParamList.
1895func (d *dbBase) ReadValues(ctx context.Context, q dbQuerier, qs *querySet, mi *models.ModelInfo, cond *Condition, exprs []string, container interface{}, tz *time.Location) (int64, error) {1896 var (
1897 maps []Params
1898 lists []ParamsList
func setFieldValue
has a cyclomatic complexity of 73 with "critical" risk1701}
1702
1703// Set one value to struct column field.
1704func (d *dbBase) setFieldValue(fi *models.FieldInfo, value interface{}, field reflect.Value) (interface{}, error) {1705 fieldType := fi.FieldType
1706 isNative := !fi.IsFielder
1707
func convertValueFromDB
has a cyclomatic complexity of 57 with "critical" risk1537}
1538
1539// convert value from database result to value following in field type.
1540func (d *dbBase) convertValueFromDB(fi *models.FieldInfo, val interface{}, tz *time.Location) (interface{}, error) {1541 if val == nil {
1542 return nil, nil
1543 }
func GenerateOperatorSQL
has a cyclomatic complexity of 18 with "high" risk1450}
1451
1452// GenerateOperatorSQL generate sql with replacing operator string placeholders and replaced values.
1453func (d *dbBase) GenerateOperatorSQL(mi *models.ModelInfo, fi *models.FieldInfo, operator string, args []interface{}, tz *time.Location) (string, []interface{}) {1454 var sql string
1455 params := getFlatParams(fi, args, tz)
1456
A function with high cyclomatic complexity can be hard to understand and maintain. Cyclomatic complexity is a software metric that measures the number of independent paths through a function. A higher cyclomatic complexity indicates that the function has more decision points and is more complex.
Functions with high cyclomatic complexity are more likely to have bugs and be harder to test. They may lead to reduced code maintainability and increased development time.
To reduce the cyclomatic complexity of a function, you can:
package main
import "log"
func fizzbuzzfuzz(x int) { // cc = 1
if x == 0 || x < 0 { // cc = 3 (if, ||)
return
}
for i := 1; i <= x; i++ { // cc = 4 (for)
switch i % 15 * 2 {
case 0: // cc = 5 (case)
countDiv3 += 1
countDiv5 += 1
log.Println("fizzbuzz")
break
case 3:
case 6:
case 9:
case 12: // cc = 9 (case)
countDiv3 += 1
log.Println("fizz")
break
case 5:
case 10: // cc = 11 (case)
countDiv5 += 1
log.Println("buzz")
break
default:
log.Printf("%d\n", x)
}
}
} // CC == 11; raises issues
package main
import "log"
func fizzbuzz(x int) { // cc = 1
for i := 1; i <= x; i++ { // cc = 2 (for)
y := i%3 == 0
z := i%5 == 0
if y == z { // 3
if y == false { // 4
log.Printf("%d\n", i)
} else {
log.Println("fizzbuzz")
}
} else {
if y { // 5
log.Println("fizz")
} else {
log.Println("buzz")
}
}
}
} // CC == 5
Cyclomatic complexity threshold can be configured using the
cyclomatic_complexity_threshold
(docs) in the
.deepsource.toml
config file.
Configuring this is optional. If you don't provide a value, the Analyzer will
raise issues for functions with complexity higher than the default threshold,
which is medium
(only raise issues for >15) for the Go Analyzer.
Here's the mapping of the risk category to the cyclomatic complexity score to help you configure this better:
Risk category | Cyclomatic complexity range | Recommended action |
---|---|---|
low | 1-5 | No action needed. |
medium | 6-15 | Review and monitor. |
high | 16-25 | Review and refactor. Recommended to add comments if the function is absolutely needed to be kept as it is. |
very-high. | 26-50 | Refactor to reduce the complexity. |
critical | >50 | Must refactor this. This can make the code untestable and very difficult to understand. |