Delta analysis

Reviewed on Mar 15, 06:16

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Code Health
4.97
(+0.19)
Code Health Quality gate
Failed
Declining Code Health
2 findings
Improving Code Health
13 findings
Affected Hotspots
5 Files
Authors

DS

Declining Code Health
2 FINDINGS
(1 type in 2 files)
Files with highest declining Code Health findings are at the top.

AvoidanceModule::plan already has high cyclomatic complexity, and now it increases in Lines of Code from 104 to 106

A Complex Method has a high cyclomatic complexity. The recommended threshold for the C++ language is a cyclomatic complexity lower than 9.

There are many reasons for Complex Method. Sometimes, another design approach is beneficial such as a) modeling state using an explicit state machine rather than conditionals, or b) using table lookup rather than long chains of logic. In other scenarios, the function can be split using EXTRACT FUNCTION. Just make sure you extract natural and cohesive functions. Complex Methods can also be addressed by identifying complex conditional expressions and then using the DECOMPOSE CONDITIONAL refactoring.

This code health issue has been solved before in this project. Here are some examples for inspiration:

shapes.cpp (e69b61df)
(+0.90)
...
Author: Taekjin LEE <taekjin.lee@tier4.jp>
Date: 2025-03-16 15:01+00:00
refactor(multi_object_tracker): internal message driven process (#10203)
* refactor(multi_object_tracker): streamline input channel configuration handling feat(multi_object_tracker): introduce InputChannel struct for input channel configuration refactor(multi_object_tracker): improve marker handling and initialization in TrackerObjectDebugger feat(multi_object_tracker): enhance InputChannel with trust flags for object properties refactor(multi_object_tracker): remove unused channel_size parameter from tracker constructors feat(multi_object_tracker): update InputChannel flags to trust object extension and classification fix(multi_object_tracker): replace channel.index with channel_index for consistency feat(multi_object_tracker): update TrackerObjectDebugger and TrackerProcessor to accept channels_config parameter refactor(multi_object_tracker): remove redundant existence probability initialization from tracker constructors feat(multi_object_tracker): integrate data association into TrackerProcessor and add associate method feat(multi_object_tracker): enhance updateWithMeasurement to include channel_info for improved classification handling refactor(multi_object_tracker): replace object_id with uuid in DynamicObject and related classes fix(multi_object_tracker): update UUID handling in Tracker to use uuid_msg for consistency refactor(multi_object_tracker): simplify pose and covariance handling in tracker classes refactor(multi_object_tracker): replace pose_with_covariance with separate pose and covariance attributes in DynamicObject refactor: remove z state from tracker. it will uses object state refactor(multi_object_tracker): streamline object handling in trackers and remove unnecessary shape processing refactor(multi_object_tracker): remove z position handling from trackers and update object kinematics structure refactor(multi_object_tracker): remove BoundingBox structure from trackers and implement object extension limits refactor(multi_object_tracker): remove unnecessary blank lines in tracker getTrackedObject methods refactor(multi_object_tracker): simplify input channel configuration by removing trust flags and consolidating parameters Signed-off-by: Taekjin LEE <taekjin.lee@tier4.jp> * refactor(multi_object_tracker): use const reference in loop and simplify tracker update logic Signed-off-by: Taekjin LEE <taekjin.lee@tier4.jp> * refactor(multi_object_tracker): update shape handling and streamline object tracking logic Signed-off-by: Taekjin LEE <taekjin.lee@tier4.jp> * refactor(multi_object_tracker): update shape handling to use geometry_msgs::msg::Point for anchor vectors Signed-off-by: Taekjin LEE <taekjin.lee@tier4.jp> * style(pre-commit): autofix Signed-off-by: Taekjin LEE <taekjin.lee@tier4.jp> * refactor(multi_object_tracker): modify getNearestCornerOrSurface function signature and update related logic Signed-off-by: Taekjin LEE <taekjin.lee@tier4.jp> refactor(multi_object_tracker): remove self_transform parameter from measure and update methods refactor(multi_object_tracker): update calcAnchorPointOffset function signature and streamline object handling refactor(multi_object_tracker): set shape type to BOUNDING_BOX for object trackers --------- Signed-off-by: Taekjin LEE <taekjin.lee@tier4.jp> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
@@ -13,5 +13,2 @@
// limitations under the License.
//
//
// Author: v1.0 Yukihiro Saito, Taekjin Lee
@@ -69,7 +66,5 @@ double get2dIoU(
const auto source_polygon = autoware_utils::to_polygon2d(
source_object.kinematics.pose_with_covariance.pose, source_object.shape);
const auto source_polygon = autoware_utils::to_polygon2d(source_object.pose, source_object.shape);
if (boost::geometry::area(source_polygon) < MIN_AREA) return 0.0;
const auto target_polygon = autoware_utils::to_polygon2d(
target_object.kinematics.pose_with_covariance.pose, target_object.shape);
const auto target_polygon = autoware_utils::to_polygon2d(target_object.pose, target_object.shape);
if (boost::geometry::area(target_polygon) < MIN_AREA) return 0.0;
@@ -113,6 +108,4 @@ bool convertConvexHullToBoundingBox(
// calc new center
const Eigen::Vector2d center{
input_object.kinematics.pose_with_covariance.pose.position.x,
input_object.kinematics.pose_with_covariance.pose.position.y};
const auto yaw = tf2::getYaw(input_object.kinematics.pose_with_covariance.pose.orientation);
const Eigen::Vector2d center{input_object.pose.position.x, input_object.pose.position.y};
const auto yaw = tf2::getYaw(input_object.pose.orientation);
const Eigen::Matrix2d R_inv = Eigen::Rotation2Dd(-yaw).toRotationMatrix();
@@ -123,4 +116,4 @@ bool convertConvexHullToBoundingBox(
output_object = input_object;
output_object.kinematics.pose_with_covariance.pose.position.x = new_center.x();
output_object.kinematics.pose_with_covariance.pose.position.y = new_center.y();
output_object.pose.position.x = new_center.x();
output_object.pose.position.y = new_center.y();
@@ -137,3 +130,3 @@ bool getMeasurementYaw(
{
measurement_yaw = tf2::getYaw(object.kinematics.pose_with_covariance.pose.orientation);
measurement_yaw = tf2::getYaw(object.pose.orientation);
@@ -180,6 +173,11 @@ enum BBOX_IDX {
*/
int getNearestCornerOrSurface(
const double x, const double y, const double yaw, const double width, const double length,
const geometry_msgs::msg::Transform & self_transform)
void getNearestCornerOrSurface(
const geometry_msgs::msg::Transform & self_transform, types::DynamicObject & object)
{
const double x = object.pose.position.x;
const double y = object.pose.position.y;
const double yaw = tf2::getYaw(object.pose.orientation);
const double width = object.shape.dimensions.y;
const double length = object.shape.dimensions.x;
// get local vehicle pose
@@ -193,3 +191,3 @@ int getNearestCornerOrSurface(
// Determine Index
// Determine anchor point
// x+ (front)
@@ -200,104 +198,57 @@ int getNearestCornerOrSurface(
// x- (rear)
int xgrid = 0;
int ygrid = 0;
const int labels[3][3] = {
{BBOX_IDX::FRONT_L_CORNER, BBOX_IDX::FRONT_SURFACE, BBOX_IDX::FRONT_R_CORNER},
{BBOX_IDX::LEFT_SURFACE, BBOX_IDX::INSIDE, BBOX_IDX::RIGHT_SURFACE},
{BBOX_IDX::REAR_L_CORNER, BBOX_IDX::REAR_SURFACE, BBOX_IDX::REAR_R_CORNER}};
double anchor_x = 0;
double anchor_y = 0;
if (xl > length / 2.0) {
xgrid = 0; // front
anchor_x = length / 2.0;
} else if (xl > -length / 2.0) {
xgrid = 1; // middle
anchor_x = 0;
} else {
xgrid = 2; // rear
anchor_x = -length / 2.0;
}
if (yl > width / 2.0) {
ygrid = 0; // left
anchor_y = width / 2.0;
} else if (yl > -width / 2.0) {
ygrid = 1; // middle
anchor_y = 0;
} else {
ygrid = 2; // right
anchor_y = -width / 2.0;
}
return labels[xgrid][ygrid]; // 0 to 7 + 1(null) value
}
/**
* @brief Calc bounding box center offset caused by shape change
* @param dw: width update [m] = w_new - w_old
* @param dl: length update [m] = l_new - l_old
* @param indx: nearest corner index
* @return 2d offset vector caused by shape change
*/
inline Eigen::Vector2d calcOffsetVectorFromShapeChange(
const double dw, const double dl, const int indx)
{
Eigen::Vector2d offset;
// if surface
if (indx == BBOX_IDX::FRONT_SURFACE) {
offset(0, 0) = dl / 2.0; // move forward
offset(1, 0) = 0;
} else if (indx == BBOX_IDX::RIGHT_SURFACE) {
offset(0, 0) = 0;
offset(1, 0) = -dw / 2.0; // move right
} else if (indx == BBOX_IDX::REAR_SURFACE) {
offset(0, 0) = -dl / 2.0; // move backward
offset(1, 0) = 0;
} else if (indx == BBOX_IDX::LEFT_SURFACE) {
offset(0, 0) = 0;
offset(1, 0) = dw / 2.0; // move left
}
// if corner
if (indx == BBOX_IDX::FRONT_R_CORNER) {
offset(0, 0) = dl / 2.0; // move forward
offset(1, 0) = -dw / 2.0; // move right
} else if (indx == BBOX_IDX::REAR_R_CORNER) {
offset(0, 0) = -dl / 2.0; // move backward
offset(1, 0) = -dw / 2.0; // move right
} else if (indx == BBOX_IDX::REAR_L_CORNER) {
offset(0, 0) = -dl / 2.0; // move backward
offset(1, 0) = dw / 2.0; // move left
} else if (indx == BBOX_IDX::FRONT_L_CORNER) {
offset(0, 0) = dl / 2.0; // move forward
offset(1, 0) = dw / 2.0; // move left
}
return offset; // do nothing if indx == INVALID or INSIDE
object.anchor_point.x = anchor_x;
object.anchor_point.y = anchor_y;
}
/**
* @brief Convert input object center to tracking point based on nearest corner information
* 1. update anchor offset vector, 2. offset input bbox based on tracking_offset vector and
* prediction yaw angle
* @param w: last input bounding box width
* @param l: last input bounding box length
* @param indx: last input bounding box closest corner index
* @param input_object: input object bounding box
* @param yaw: current yaw estimation
* @param offset_object: output tracking measurement to feed ekf
* @return nearest corner index(int)
*/
void calcAnchorPointOffset(
const double w, const double l, const int indx, const types::DynamicObject & input_object,
const double & yaw, types::DynamicObject & offset_object, Eigen::Vector2d & tracking_offset)
const types::DynamicObject & this_object, Eigen::Vector2d & tracking_offset,
types::DynamicObject & updating_object)
{
// copy value
offset_object = input_object;
// invalid index
if (indx == BBOX_IDX::INSIDE) {
return; // do nothing
const geometry_msgs::msg::Point anchor_vector = updating_object.anchor_point;
// invalid anchor
if (anchor_vector.x <= 1e-6 && anchor_vector.y <= 1e-6) {
return;
}
double input_yaw = tf2::getYaw(updating_object.pose.orientation);
// current object width and height
const double w_n = input_object.shape.dimensions.y;
const double l_n = input_object.shape.dimensions.x;
const double length = this_object.shape.dimensions.x;
const double width = this_object.shape.dimensions.y;
// update offset
const Eigen::Vector2d offset = calcOffsetVectorFromShapeChange(w_n - w, l_n - l, indx);
tracking_offset = offset;
tracking_offset = Eigen::Vector2d(anchor_vector.x, anchor_vector.y);
if (tracking_offset.x() > 0) {
tracking_offset.x() -= length / 2.0;
} else if (tracking_offset.x() < 0) {
tracking_offset.x() += length / 2.0;
}
if (tracking_offset.y() > 0) {
tracking_offset.y() -= width / 2.0;
} else if (tracking_offset.y() < 0) {
tracking_offset.y() += width / 2.0;
}
// offset input object
const Eigen::Matrix2d R = Eigen::Rotation2Dd(yaw).toRotationMatrix();
const Eigen::Matrix2d R = Eigen::Rotation2Dd(input_yaw).toRotationMatrix();
const Eigen::Vector2d rotated_offset = R * tracking_offset;
offset_object.kinematics.pose_with_covariance.pose.position.x += rotated_offset.x();
offset_object.kinematics.pose_with_covariance.pose.position.y += rotated_offset.y();
updating_object.pose.position.x += rotated_offset.x();
updating_object.pose.position.y += rotated_offset.y();
}

TurnSignalDecider::getBehaviorTurnSignalInfo has a cyclomatic complexity of 23, threshold = 9

A Complex Method has a high cyclomatic complexity. The recommended threshold for the C++ language is a cyclomatic complexity lower than 9.

There are many reasons for Complex Method. Sometimes, another design approach is beneficial such as a) modeling state using an explicit state machine rather than conditionals, or b) using table lookup rather than long chains of logic. In other scenarios, the function can be split using EXTRACT FUNCTION. Just make sure you extract natural and cohesive functions. Complex Methods can also be addressed by identifying complex conditional expressions and then using the DECOMPOSE CONDITIONAL refactoring.

This code health issue has been solved before in this project. Here are some examples for inspiration:

shapes.cpp (e69b61df)
(+0.90)
...
Author: Taekjin LEE <taekjin.lee@tier4.jp>
Date: 2025-03-16 15:01+00:00
refactor(multi_object_tracker): internal message driven process (#10203)
* refactor(multi_object_tracker): streamline input channel configuration handling feat(multi_object_tracker): introduce InputChannel struct for input channel configuration refactor(multi_object_tracker): improve marker handling and initialization in TrackerObjectDebugger feat(multi_object_tracker): enhance InputChannel with trust flags for object properties refactor(multi_object_tracker): remove unused channel_size parameter from tracker constructors feat(multi_object_tracker): update InputChannel flags to trust object extension and classification fix(multi_object_tracker): replace channel.index with channel_index for consistency feat(multi_object_tracker): update TrackerObjectDebugger and TrackerProcessor to accept channels_config parameter refactor(multi_object_tracker): remove redundant existence probability initialization from tracker constructors feat(multi_object_tracker): integrate data association into TrackerProcessor and add associate method feat(multi_object_tracker): enhance updateWithMeasurement to include channel_info for improved classification handling refactor(multi_object_tracker): replace object_id with uuid in DynamicObject and related classes fix(multi_object_tracker): update UUID handling in Tracker to use uuid_msg for consistency refactor(multi_object_tracker): simplify pose and covariance handling in tracker classes refactor(multi_object_tracker): replace pose_with_covariance with separate pose and covariance attributes in DynamicObject refactor: remove z state from tracker. it will uses object state refactor(multi_object_tracker): streamline object handling in trackers and remove unnecessary shape processing refactor(multi_object_tracker): remove z position handling from trackers and update object kinematics structure refactor(multi_object_tracker): remove BoundingBox structure from trackers and implement object extension limits refactor(multi_object_tracker): remove unnecessary blank lines in tracker getTrackedObject methods refactor(multi_object_tracker): simplify input channel configuration by removing trust flags and consolidating parameters Signed-off-by: Taekjin LEE <taekjin.lee@tier4.jp> * refactor(multi_object_tracker): use const reference in loop and simplify tracker update logic Signed-off-by: Taekjin LEE <taekjin.lee@tier4.jp> * refactor(multi_object_tracker): update shape handling and streamline object tracking logic Signed-off-by: Taekjin LEE <taekjin.lee@tier4.jp> * refactor(multi_object_tracker): update shape handling to use geometry_msgs::msg::Point for anchor vectors Signed-off-by: Taekjin LEE <taekjin.lee@tier4.jp> * style(pre-commit): autofix Signed-off-by: Taekjin LEE <taekjin.lee@tier4.jp> * refactor(multi_object_tracker): modify getNearestCornerOrSurface function signature and update related logic Signed-off-by: Taekjin LEE <taekjin.lee@tier4.jp> refactor(multi_object_tracker): remove self_transform parameter from measure and update methods refactor(multi_object_tracker): update calcAnchorPointOffset function signature and streamline object handling refactor(multi_object_tracker): set shape type to BOUNDING_BOX for object trackers --------- Signed-off-by: Taekjin LEE <taekjin.lee@tier4.jp> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
@@ -13,5 +13,2 @@
// limitations under the License.
//
//
// Author: v1.0 Yukihiro Saito, Taekjin Lee
@@ -69,7 +66,5 @@ double get2dIoU(
const auto source_polygon = autoware_utils::to_polygon2d(
source_object.kinematics.pose_with_covariance.pose, source_object.shape);
const auto source_polygon = autoware_utils::to_polygon2d(source_object.pose, source_object.shape);
if (boost::geometry::area(source_polygon) < MIN_AREA) return 0.0;
const auto target_polygon = autoware_utils::to_polygon2d(
target_object.kinematics.pose_with_covariance.pose, target_object.shape);
const auto target_polygon = autoware_utils::to_polygon2d(target_object.pose, target_object.shape);
if (boost::geometry::area(target_polygon) < MIN_AREA) return 0.0;
@@ -113,6 +108,4 @@ bool convertConvexHullToBoundingBox(
// calc new center
const Eigen::Vector2d center{
input_object.kinematics.pose_with_covariance.pose.position.x,
input_object.kinematics.pose_with_covariance.pose.position.y};
const auto yaw = tf2::getYaw(input_object.kinematics.pose_with_covariance.pose.orientation);
const Eigen::Vector2d center{input_object.pose.position.x, input_object.pose.position.y};
const auto yaw = tf2::getYaw(input_object.pose.orientation);
const Eigen::Matrix2d R_inv = Eigen::Rotation2Dd(-yaw).toRotationMatrix();
@@ -123,4 +116,4 @@ bool convertConvexHullToBoundingBox(
output_object = input_object;
output_object.kinematics.pose_with_covariance.pose.position.x = new_center.x();
output_object.kinematics.pose_with_covariance.pose.position.y = new_center.y();
output_object.pose.position.x = new_center.x();
output_object.pose.position.y = new_center.y();
@@ -137,3 +130,3 @@ bool getMeasurementYaw(
{
measurement_yaw = tf2::getYaw(object.kinematics.pose_with_covariance.pose.orientation);
measurement_yaw = tf2::getYaw(object.pose.orientation);
@@ -180,6 +173,11 @@ enum BBOX_IDX {
*/
int getNearestCornerOrSurface(
const double x, const double y, const double yaw, const double width, const double length,
const geometry_msgs::msg::Transform & self_transform)
void getNearestCornerOrSurface(
const geometry_msgs::msg::Transform & self_transform, types::DynamicObject & object)
{
const double x = object.pose.position.x;
const double y = object.pose.position.y;
const double yaw = tf2::getYaw(object.pose.orientation);
const double width = object.shape.dimensions.y;
const double length = object.shape.dimensions.x;
// get local vehicle pose
@@ -193,3 +191,3 @@ int getNearestCornerOrSurface(
// Determine Index
// Determine anchor point
// x+ (front)
@@ -200,104 +198,57 @@ int getNearestCornerOrSurface(
// x- (rear)
int xgrid = 0;
int ygrid = 0;
const int labels[3][3] = {
{BBOX_IDX::FRONT_L_CORNER, BBOX_IDX::FRONT_SURFACE, BBOX_IDX::FRONT_R_CORNER},
{BBOX_IDX::LEFT_SURFACE, BBOX_IDX::INSIDE, BBOX_IDX::RIGHT_SURFACE},
{BBOX_IDX::REAR_L_CORNER, BBOX_IDX::REAR_SURFACE, BBOX_IDX::REAR_R_CORNER}};
double anchor_x = 0;
double anchor_y = 0;
if (xl > length / 2.0) {
xgrid = 0; // front
anchor_x = length / 2.0;
} else if (xl > -length / 2.0) {
xgrid = 1; // middle
anchor_x = 0;
} else {
xgrid = 2; // rear
anchor_x = -length / 2.0;
}
if (yl > width / 2.0) {
ygrid = 0; // left
anchor_y = width / 2.0;
} else if (yl > -width / 2.0) {
ygrid = 1; // middle
anchor_y = 0;
} else {
ygrid = 2; // right
anchor_y = -width / 2.0;
}
return labels[xgrid][ygrid]; // 0 to 7 + 1(null) value
}
/**
* @brief Calc bounding box center offset caused by shape change
* @param dw: width update [m] = w_new - w_old
* @param dl: length update [m] = l_new - l_old
* @param indx: nearest corner index
* @return 2d offset vector caused by shape change
*/
inline Eigen::Vector2d calcOffsetVectorFromShapeChange(
const double dw, const double dl, const int indx)
{
Eigen::Vector2d offset;
// if surface
if (indx == BBOX_IDX::FRONT_SURFACE) {
offset(0, 0) = dl / 2.0; // move forward
offset(1, 0) = 0;
} else if (indx == BBOX_IDX::RIGHT_SURFACE) {
offset(0, 0) = 0;
offset(1, 0) = -dw / 2.0; // move right
} else if (indx == BBOX_IDX::REAR_SURFACE) {
offset(0, 0) = -dl / 2.0; // move backward
offset(1, 0) = 0;
} else if (indx == BBOX_IDX::LEFT_SURFACE) {
offset(0, 0) = 0;
offset(1, 0) = dw / 2.0; // move left
}
// if corner
if (indx == BBOX_IDX::FRONT_R_CORNER) {
offset(0, 0) = dl / 2.0; // move forward
offset(1, 0) = -dw / 2.0; // move right
} else if (indx == BBOX_IDX::REAR_R_CORNER) {
offset(0, 0) = -dl / 2.0; // move backward
offset(1, 0) = -dw / 2.0; // move right
} else if (indx == BBOX_IDX::REAR_L_CORNER) {
offset(0, 0) = -dl / 2.0; // move backward
offset(1, 0) = dw / 2.0; // move left
} else if (indx == BBOX_IDX::FRONT_L_CORNER) {
offset(0, 0) = dl / 2.0; // move forward
offset(1, 0) = dw / 2.0; // move left
}
return offset; // do nothing if indx == INVALID or INSIDE
object.anchor_point.x = anchor_x;
object.anchor_point.y = anchor_y;
}
/**
* @brief Convert input object center to tracking point based on nearest corner information
* 1. update anchor offset vector, 2. offset input bbox based on tracking_offset vector and
* prediction yaw angle
* @param w: last input bounding box width
* @param l: last input bounding box length
* @param indx: last input bounding box closest corner index
* @param input_object: input object bounding box
* @param yaw: current yaw estimation
* @param offset_object: output tracking measurement to feed ekf
* @return nearest corner index(int)
*/
void calcAnchorPointOffset(
const double w, const double l, const int indx, const types::DynamicObject & input_object,
const double & yaw, types::DynamicObject & offset_object, Eigen::Vector2d & tracking_offset)
const types::DynamicObject & this_object, Eigen::Vector2d & tracking_offset,
types::DynamicObject & updating_object)
{
// copy value
offset_object = input_object;
// invalid index
if (indx == BBOX_IDX::INSIDE) {
return; // do nothing
const geometry_msgs::msg::Point anchor_vector = updating_object.anchor_point;
// invalid anchor
if (anchor_vector.x <= 1e-6 && anchor_vector.y <= 1e-6) {
return;
}
double input_yaw = tf2::getYaw(updating_object.pose.orientation);
// current object width and height
const double w_n = input_object.shape.dimensions.y;
const double l_n = input_object.shape.dimensions.x;
const double length = this_object.shape.dimensions.x;
const double width = this_object.shape.dimensions.y;
// update offset
const Eigen::Vector2d offset = calcOffsetVectorFromShapeChange(w_n - w, l_n - l, indx);
tracking_offset = offset;
tracking_offset = Eigen::Vector2d(anchor_vector.x, anchor_vector.y);
if (tracking_offset.x() > 0) {
tracking_offset.x() -= length / 2.0;
} else if (tracking_offset.x() < 0) {
tracking_offset.x() += length / 2.0;
}
if (tracking_offset.y() > 0) {
tracking_offset.y() -= width / 2.0;
} else if (tracking_offset.y() < 0) {
tracking_offset.y() += width / 2.0;
}
// offset input object
const Eigen::Matrix2d R = Eigen::Rotation2Dd(yaw).toRotationMatrix();
const Eigen::Matrix2d R = Eigen::Rotation2Dd(input_yaw).toRotationMatrix();
const Eigen::Vector2d rotated_offset = R * tracking_offset;
offset_object.kinematics.pose_with_covariance.pose.position.x += rotated_offset.x();
offset_object.kinematics.pose_with_covariance.pose.position.y += rotated_offset.y();
updating_object.pose.position.x += rotated_offset.x();
updating_object.pose.position.y += rotated_offset.y();
}

The lines of code increases from 1192 to 1194, improve code health by reducing it to 1000

This module has 1194 lines of code (comments stripped away). This puts the module at risk of evolving into a Brain Class. Brain Classes are problematic since changes become more complex over time, harder to test, and challenging to refactor. Act now to prevent future maintenance issues.

Look for opportunities to modularize the design. This is done by identifying groups of functions that represent different responsibilities and/or operate on different data. Once you have identified the different responsibilities, then use refactorings like EXTRACT CLASS.

The mean cyclomatic complexity increases from 7.25 to 8.46, threshold = 4

Overall Code Complexity is measured by the mean cyclomatic complexity across all functions in the file. The lower the number, the better.

Cyclomatic complexity is a function level metric that measures the number of logical branches (if-else, loops, etc.). Cyclomatic complexity is a rough complexity measure, but useful as a way of estimating the minimum number of unit tests you would need. As such, prefer functions with low cyclomatic complexity (2-3 branches).

You address the overall cyclomatic complexity by a) modularizing the code, and b) abstract away the complexity. Let's look at some examples:

Modularizing the Code: Do an X-Ray and inspect the local hotspots. Are there any complex conditional expressions? If yes, then do a DECOMPOSE CONDITIONAL refactoring. Extract the conditional logic into a separate function and put a good name on that function. This clarifies the intent and makes the original function easier to read. Repeat until all complex conditional expressions have been simplified.

The lines of code increases from 1653 to 1657, improve code health by reducing it to 1000

This module has 1657 lines of code (comments stripped away). This puts the module at risk of evolving into a Brain Class. Brain Classes are problematic since changes become more complex over time, harder to test, and challenging to refactor. Act now to prevent future maintenance issues.

Look for opportunities to modularize the design. This is done by identifying groups of functions that represent different responsibilities and/or operate on different data. Once you have identified the different responsibilities, then use refactorings like EXTRACT CLASS.

The lines of code increases from 1717 to 1738, improve code health by reducing it to 1000

This module has 1738 lines of code (comments stripped away). This puts the module at risk of evolving into a Brain Class. Brain Classes are problematic since changes become more complex over time, harder to test, and challenging to refactor. Act now to prevent future maintenance issues.

Look for opportunities to modularize the design. This is done by identifying groups of functions that represent different responsibilities and/or operate on different data. Once you have identified the different responsibilities, then use refactorings like EXTRACT CLASS.

The mean cyclomatic complexity increases from 5.06 to 5.09, threshold = 4

Overall Code Complexity is measured by the mean cyclomatic complexity across all functions in the file. The lower the number, the better.

Cyclomatic complexity is a function level metric that measures the number of logical branches (if-else, loops, etc.). Cyclomatic complexity is a rough complexity measure, but useful as a way of estimating the minimum number of unit tests you would need. As such, prefer functions with low cyclomatic complexity (2-3 branches).

You address the overall cyclomatic complexity by a) modularizing the code, and b) abstract away the complexity. Let's look at some examples:

Modularizing the Code: Do an X-Ray and inspect the local hotspots. Are there any complex conditional expressions? If yes, then do a DECOMPOSE CONDITIONAL refactoring. Extract the conditional logic into a separate function and put a good name on that function. This clarifies the intent and makes the original function easier to read. Repeat until all complex conditional expressions have been simplified.

The lines of code decreases from 1895 to 1711, improve code health by reducing it to 1000

existShiftSideLane is no longer above the threshold for cyclomatic complexity

calcTurnSignalInfo is no longer above the threshold for cyclomatic complexity

existShiftSideLane is no longer above the threshold for logical blocks with deeply nested code

The mean cyclomatic complexity decreases from 4.96 to 4.76, threshold = 4

existShiftSideLane is no longer above the threshold for number of arguments

isNearEndOfShift is no longer above the threshold for number of arguments

calcTurnSignalInfo is no longer above the threshold for number of arguments

The number of functions in this module is no longer above the threshold

The lines of code decreases from 1369 to 1340, improve code health by reducing it to 1000

StartPlannerModule::calcTurnSignalInfo is no longer above the threshold for cyclomatic complexity

StartPlannerModule::calcTurnSignalInfo no longer has a complex conditional

StartPlannerModule::calcTurnSignalInfo is no longer above the threshold for logical blocks with deeply nested code