Options For Handling Small Tasks And Internal Improvements
Improving Efficiency of Small Tasks
Identifying Areas for Improvement
When looking to optimize workflows, identifying frequent small tasks that could benefit from improvements is a key first step. This section covers techniques for pinpointing areas where minor enhancements could reduce repetition and save significant time across an entire codebase.
Reviewing frequent minor coding tasks
Analyze git commit logs and pull requests to see patterns of mundane coding work that get implemented repeatedly for different features. Examples include null checking, input sanitization, type checking, logging, and simple data transformations. These repetitive code snippets are prime candidates for refactoring into reusable methods that reduce duplication.
Pinpointing repetitive logical checks
Trace the execution paths of your code to find duplicated conditional checks and validation logic. Extract these repetitive guard clause checks out into reusable functions that accept parameters to evaluate. This eliminates endless copying of the same defensive coding patterns across the codebase.
Finding common debugging needs
Note areas during development that require excessive debugging to understand program state and flow. Identify checks like printing variables, logging method entries, and asserting on key conditions that get added and removed. These temporary debug statements indicate where encapsulating complexity through abstractions can permanently improve understanding and maintainability.
Techniques for Optimization
Once opportunities for improvement have been identified, a variety of techniques can be employed to eliminate repetition. This section covers different ways to optimize and consolidate redundant coding patterns.
Creating helper methods and classes
Extract segments of duplicated code into reusable helper functions and classes. Consolidate shared logic related to data validation, manipulation, default settings, error handling, and other cross-cutting concerns into these helpers. Call the helpers instead of repeating code.
Caching intermediate results
Store the results of expensive operations in memory to avoid unnecessary recomputation. This requires identifying parts of the program that repeat the same CPU and I/O intensive tasks instead of caching. Use data structures like dictionaries and sets to store cached data.
Using memoization
Memoization is an optimization technique that caches return values of a function based on its parameters. Replace functions that repeatedly compute the same outputs for given inputs with memoized variants that store prior call values in a lookup table. This elicits major performance gains.
Employing lazily initialized properties
Reduce initialization costs by only creating resource intensive objects when they are first accessed. Define properties that transparently run expensive construction logic only when first called. Store the created instance for subsequent reuse instead of repeating object creation.
Implementing Internal Libraries
Consolidating helper code into well-designed shared libraries with standard interfaces promotes code reuse across projects. This section explores considerations when implementing internal utilities.
Determining shared utility functions
Survey projects to identify common areas of overlapping utility needs – like date manipulation, string formatting, data validation, etc. Cluster these utils into modular libraries with clear functional areas to prevent scope creep.
Designing a minimal internal API
Design a lightweight but extensible public interface for util libraries. Methods should have clear and descriptive names expressing intent. Accept generically useful parameter types like strings and collections when possible. Cover basic needs before extending functionality.
Encapsulating complexity
Hide elaborate implementation details behind clean and simple method signatures. Method code should robustly handle edge cases and validation internally so callers avoid repetition. Classify utilities as private when used only internally.
Facilitating code reuse
Standardize environments and provide easy integration guides to maximize adoption. Utilize semantic versioning with clear deprecation policies so improvements do not break consumers. Make utils discoverable by advertising availability and terminology.
Measuring Improvements
Quantifying optimization impact is key for demonstrating value and identifying areas requiring further enhancement. This section outlines techniques for tracking changes in efficiency.
Tracking time spent on tasks
Record timings for program flows before and after optimization using utilities like provide detailed metrics on performance gains and help identify outlier cases needing improvement.
Benchmarking before and after
Use unit tests to benchmark key code paths accessing optimized logic vs original form. Assert optimized versions meet speed and efficiency KPIs. Profile benchmarks to prevent unintended slowdowns.
Quantifying reduction in duplicates
Static analysis tools can report on duplicate code detection across projects. Establish a baseline level and track this metric over time as shared utils get adopted to showcase consolidation gains.
Monitoring adoption and usage
Instrument internal libraries to record consumption. Measure number of projects, files, classes, and methods using utils over time. Rising utilization indicates value provided and helps prioritize enhancement areas.
