Software Analysis Overview

Principles

Definition

Software analysis is a research field and methodological approach dedicated to understanding the properties and behavior of software systems. It involves the systematic examination of software artifacts, typically code or execution traces, often through automated techniques, to determine characteristics related to correctness, security, performance, and reliability. This investigation is fundamental for identifying defects, verifying system properties, and ensuring software quality throughout its lifecycle.

Ontological type

Core Methods

Applications

Tooling Ecosystem

Key developments

Key Figures

Rigorous Validation Foundations era

Mark Weiser [1] is associated with University of Maryland, College Park [3] and University of Veterinary Medicine Vienna [4] during the Rigorous Validation Foundations era. Weiser [1] introduced Program Slicing [6] (1984), a method enabling focused analysis and formal reasoning about program behavior, which proved central to rigorous software validation in this era. Victor R. Basili [2] is affiliated with University of California, Irvine [5] and University of Maryland, College Park [3] during this era. Basili [2] advanced empirical software engineering through A Methodology for Collecting Valid Software Engineering Data [7], continued with Software errors and complexity: an empirical investigation [8], and culminated in Evaluating Software Development by Analysis of Changes: Some Data from the Software Engineering Laboratory [9], collectively fostering data‑driven reliability modeling and systematic quality assurance for the era.

Architecture-Centric Analysis era

Vikram Adve [1], associated with Rice University [3] and the University of Illinois Urbana-Champaign [4] during the Architecture-Centric Analysis era, helped advance lifelong program analysis and reusable tooling. His 2004 LLVM: a compilation framework for lifelong program analysis & transformation [7] established a modular infrastructure that enabled scalable, interprocedural, and cross-language analyses central to architecture-centric methods. Dawn Song [2], affiliated with the University of California, Berkeley [5] and the University of Pittsburgh [6] during this era, contributed to dynamic security analyses that shaped architecture-aware defense strategies. Her 2005 Dynamic Taint Analysis for Automatic Detection, Analysis, and Signature Generation of Exploits on Commodity Software [8] pioneered practical dynamic analysis for automated exploit detection and signature generation, a landmark for security-centric analyses in this era.

Data-Driven ML Analysis era

Xuxian Jiang [1] is a prominent security researcher active in the Data-Driven ML Analysis era (2010–2023) with affiliations at North Carolina State University [3] and Shanghai University [4]. In this era, Jiang's Dissecting Android Malware: Characterization and Evolution [7] advanced the semantic understanding and evolution tracking of Android malware, a foundational step for data-driven analysis of mobile threats. Konrad Rieck [2] is a leading figure in software security whose work in this era spanned institutions such as University of Göttingen [5] and Technische Universität Berlin [6]. His paper Drebin: Effective and Explainable Detection of Android Malware in Your Pocket [8] demonstrated effective and explainable Android malware detection, illustrating how data-driven models can scale security analysis and defenses in the era.