A taxonomy for software change impact analysis

Lehnert, Steffen

doi:10.1145/2024445.2024454

Cited by 90 publications

(73 citation statements)

References 136 publications

(171 reference statements)

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“…Lenhert [24] presented a taxonomy of impact analysis methods. Based on a broad literature review, he presents a multitude of criteriaranging from the scope of analysis, the granularity of changes and impact, up to the availability of tool support-to classify and compare various impact analysis approaches.…”

Section: Related Workmentioning

confidence: 99%

Semantic analysis and verification of context-driven adaptive applications in intelligent environments

Preuveneers

Joosen

2016

J Reliable Intell Environ

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With the advent of the Internet of Things (IoT) and Cyber-Physical Systems (CPS), assistive applications and smart environments can tap into a growing amount of context information to learn about their users, their surroundings and typical behavior. This information is useful to adapt intelligently, autonomously and non-intrusively to a myriad of circumstances. However, trustworthiness and reliable adaptation to changes in line with user expectationsespecially to situations that the developers did not anticipate-remain key concerns. Understanding the impact of changes from a developer (design time) and system (runtime) perspective, and ensuring that no undesired side effects take place are two non-trivial research challenges to increase the adoption of such applications. Given the limited tool support for anticipating change at design time and runtime, we present our change impact analysis (CIA) methodologyfound in the formal semantic modeling of intelligent environments and rule-based application behavior-to contribute to the development and deployment of reliable context-aware adaptive applications. We validate our contributions on nontrivial smart home and office scenarios, and demonstrate how our framework helps increase trust in intelligent environment applications by anticipating change implications upfront at design time and by minimizing the occurrence of undesired side effects at runtime.

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Section: Related Workmentioning

confidence: 99%

Semantic analysis and verification of context-driven adaptive applications in intelligent environments

Preuveneers

Joosen

2016

J Reliable Intell Environ

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“…Lehnert [1] has considered the relationship between dependence clusters and impact analysis and found that clustering can be used to determine the ripple-effect during software maintenance. Beszédes et al [9] looked at the relationship between SEB dependencies, and software maintenance and found that SEB can be used to identify hidden dependencies and thus help in many maintenance tasks, including change propagation and regression testing.…”

Section: Threats To Validitymentioning

confidence: 99%

“…The need to repair and improve software involves a number of challenging tasks such as impact analysis [1], defect detection [2], software reuse [3], [4], and regression testing [5]. Such tasks are facilitated by source code that is easily separated (e.g., loosely coupled).…”

Section: Introductionmentioning

confidence: 99%

Uncovering dependence clusters and linchpin functions

Binkley

Beszedes

Islam

et al. 2015

2015 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Abstract-Dependence clusters are (maximal) collections of mutually dependent source code entities according to some dependence relation. Their presence in software complicates many maintenance activities including testing, refactoring, and feature extraction. Despite several studies finding them common in production code, their formation, identification, and overall structure are not well understood, partly because of challenges in approximating true dependences between program entities. Previous research has considered two approximate dependence relations: a fine-grained statement-level relation using control and data dependences from a program's System Dependence Graph and a coarser relation based on function-level controlflow reachability. In principal, the first is more expensive and more precise than the second.Using a collection of twenty programs, we present an empirical investigation of the clusters identified by these two approaches.In support of the analysis, we consider hybrid cluster types that works at the coarser function-level but is based on the higher-precision statement-level dependences. The three types of clusters are compared based on their slice sets using two clustering metrics. We also perform extensive analysis of the programs to identify linchpin functions -functions primarily responsible for holding a cluster together. Results include evidence that the less expensive, coarser approaches can often be used as effective proxies for the more expensive, finer-grained approaches.Finally, the linchpin analysis shows that linchpin functions can be effectively and automatically identified.

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“…A taxonomy for impact analysis can be found in [16]. Recent approaches [1,6,8,21] rely on slicing, dependence analysis, dynamic tracing and history mining.…”

Section: Related Workmentioning

confidence: 99%

An Incremental Points-to Analysis with CFL-Reachability

Lü

Shang

Xie

et al. 2013

Lecture Notes in Computer Science

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Abstract.Developing scalable and precise points-to analyses is increasingly important for analysing and optimising object-oriented programs where pointers are used pervasively. An incremental analysis for a program updates the existing analysis information after program changes to avoid reanalysing it from scratch. This can be efficiently deployed in software development environments where code changes are often small and frequent. This paper presents an incremental approach for demand-driven context-sensitive points-to analyses based on ContextFree Language (CFL) reachability. By tracing the CFL-reachable paths traversed in computing points-to sets, we can precisely identify and recompute on demand only the points-to sets affected by the program changes made. Combined with a flexible policy for controlling the granularity of traces, our analysis achieves significant speedups with little space overhead over reanalysis from scratch when evaluated with a null dereferencing client using 14 Java benchmarks.

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A taxonomy for software change impact analysis

Cited by 90 publications

References 136 publications

Semantic analysis and verification of context-driven adaptive applications in intelligent environments

Semantic analysis and verification of context-driven adaptive applications in intelligent environments

Uncovering dependence clusters and linchpin functions

An Incremental Points-to Analysis with CFL-Reachability

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