Introduction to Garbage Collection

Jan 9, 20172 likes5,344 views

Garbage collection is a form of automatic memory management used in computer programs to reclaim memory occupied by objects that are no longer needed. John McCarthy invented garbage collection for Lisp in 1959. Languages either use garbage collection or require manual memory management through techniques like allocating and freeing memory. Common garbage collection algorithms include reference counting, mark and sweep, and generational collection. The Java Virtual Machine uses different garbage collectors like serial, parallel, concurrent mark and sweep, and Garbage First collectors to reclaim memory in the Java heap.

In computer science, garbage collection (GC) is a
form of Automatic Memory Management.
The garbage collector attempts to reclaim the
memory occupied by objects that are no longer in
use by the program.
Garbage collection was invented by John McCarthy
around 1959 to abstract away manual memory
management in Lisp.

Static vs Dynamic memory allocation
Creating an array of ten integers in C (static)
int array[10];
Creating an array of integers in C (dynamic)
int * array = malloc(N * sizeof(int));
free(array);
In C++
Foo* fooPtr = new Foo()
delete fooPtr

Languages without GC
C, C++*, D*, Objective-C*,
Rust*
Languages
Languages wit GC
Java, Go, PHP, Python, Scala*,
Haskell, ….

Garbage Collection Algorithms
Reference Counting Mark and Sweep
Copy Collection
Generational Collection

Reference Counting
Keep an extra integer (“reference count”) to every
heap-allocated data structure.
● With a new reference ++refCount.
● When a reference disappears --refCount.
● If refCount == 0, then reclaim the storage
+ Easy to implement
+ Real Time cleanup
- Additional storage
- Speed: incr, decr
- Cycles not cleaned
C++, Objective-C, Rust,
PHP, Python….

Tracing Collectors vs Reference Counting

Mark and Sweep
Mark Phase: traverses all objects,
starting with roots, and marks every
object found as alive.
Sweep Phase: traverses all
objects, reclaim the storage of
unmarked objects.
+ Handles cycles
+ Easy to implement
- Stops the world**
- Scans the entire heap

Copy Collection
+ Handles cycles
+ Automatic Compaction
- Stops the world**
- Changes addresses
- 2nd half is unused

Generational Collection
● Most objects die young
● Newer objects usually point to older objects
+ Same as Copy (unidirectional)
+ Less Objects to Copy
+ Frequency of collection
+ Faster
- Stops the world**
- Complex to implement

Java Garbage Collectors
● Serial Collector - -XX:+UseSerialGC
● Parallel Collector - -XX:+UseParallelGC
● Concurrent Mark & Sweep Collector - -XX:+UseConcMarkSweepGC
● Garbage First (G1) Collector - -XX:+UseG1GC

Serial GC
● Stops the world
● Uses single thread
● Designed for single CPU small Heap apps
● Do not use it!
● Young Gen - Copy Collector
● Old Gen - Mark and Sweep

Parallel GC (The Default Collector)
● Stops the world
● Designed to work with multiple CPUs
● Uses multiple threads
● Expect high latencies when GC runs (-XX:MaxGCPauseMillis=<N>)
● Young Gen - Copy Collector
● Old Gen - Mark and Sweep

Concurrent Mark and Sweep (CMS) GC
● Stops the world (relatively short pauses)
● Designed to work with multiple CPUs
● Uses multiple threads
● Good for low latency apps
● Young Gen - Copy Collector
● Old Gen - Concurrent Mark-Sweep

Garbage First (G1) Collector
● Heap is split into (typically 2048) smaller regions
● Avoids collecting the entire heap at once, instead collects incrementally
○ The regions that contain the most garbage are collected first
● Soft real-time garbage collector (Predictable | Configurable STW)
● Compaction is relatively easy
● Uses multiple threads and is good for > 6G heap sizes. Java 9 Default GC.
● Young Gen - Copy Collector
● Old Gen - Concurrent Mark-Sweep

Minor GC vs Major GC vs Full GC
Minor GC cleans the Young Generation
Major GC cleans the Old Generation
Full GC cleans the Young and Old Generation

JVM Options to know
java -XX:+PrintFlagsFinal -version | grep HeapSize
uintx InitialHeapSize := 268435456 {product}
uintx MaxHeapSize := 4294967296 {product}
java -Xms256m -Xmx2048m
-XX:MaxGCPauseMillis=200
-XX:+PrintGCDetails -XX:+PrintGCDateStamps

Java Memory Leak
Hashmap keys without proper equals and hashcode
Maps or lists which are growing forever

Troubleshooting
UI Options: jconsole or jvisualvm.
Command Line:
jstat -gc -t processID 1s
jmap -heap processID
jmap -dump:live,format=b,file=heap.bin processID
jhat heap.bin
JVM Options: -XX:+PrintGCDetails-XX:+PrintGCDateStamps

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TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...Andre Hora

Unittest and pytest are the most popular testing frameworks in Python. Overall, pytest provides some advantages, including simpler assertion, reuse of fixtures, and interoperability. Due to such benefits, multiple projects in the Python ecosystem have migrated from unittest to pytest. To facilitate the migration, pytest can also run unittest tests, thus, the migration can happen gradually over time. However, the migration can be timeconsuming and take a long time to conclude. In this context, projects would benefit from automated solutions to support the migration process. In this paper, we propose TestMigrationsInPy, a dataset of test migrations from unittest to pytest. TestMigrationsInPy contains 923 real-world migrations performed by developers. Future research proposing novel solutions to migrate frameworks in Python can rely on TestMigrationsInPy as a ground truth. Moreover, as TestMigrationsInPy includes information about the migration type (e.g., changes in assertions or fixtures), our dataset enables novel solutions to be verified effectively, for instance, from simpler assertion migrations to more complex fixture migrations. TestMigrationsInPy is publicly available at: https://github.com/altinoalvesjunior/TestMigrationsInPy.

Secure Test Infrastructure: The Backbone of Trustworthy Software DevelopmentShubham Joshi

What Do Contribution Guidelines Say About Software Testing? (MSR 2025)Andre Hora

Software testing plays a crucial role in the contribution process of open-source projects. For example, contributions introducing new features are expected to include tests, and contributions with tests are more likely to be accepted. Although most real-world projects require contributors to write tests, the specific testing practices communicated to contributors remain unclear. In this paper, we present an empirical study to understand better how software testing is approached in contribution guidelines. We analyze the guidelines of 200 Python and JavaScript open-source software projects. We find that 78% of the projects include some form of test documentation for contributors. Test documentation is located in multiple sources, including CONTRIBUTING files (58%), external documentation (24%), and README files (8%). Furthermore, test documentation commonly explains how to run tests (83.5%), but less often provides guidance on how to write tests (37%). It frequently covers unit tests (71%), but rarely addresses integration (20.5%) and end-to-end tests (15.5%). Other key testing aspects are also less frequently discussed: test coverage (25.5%) and mocking (9.5%). We conclude by discussing implications and future research.

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As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.

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Designing AI-Powered APIs on Azure: Best Practices& ConsiderationsDinusha Kumarasiri

AI is transforming APIs, enabling smarter automation, enhanced decision-making, and seamless integrations. This presentation explores key design principles for AI-infused APIs on Azure, covering performance optimization, security best practices, scalability strategies, and responsible AI governance. Learn how to leverage Azure API Management, machine learning models, and cloud-native architectures to build robust, efficient, and intelligent API solutions

Meet the Agents: How AI Is Learning to Think, Plan, and CollaborateMaxim Salnikov

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