Quantitative program slicing: Separating statements by relevance

Santelices, Raúl; Zhang, Yiji; Jiang, Siyuan; Cai, Haipeng; Zhang, Yingjie

doi:10.1109/icse.2013.6606695

Cited by 14 publications

(12 citation statements)

References 14 publications

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“…In [74], we outlined an early version of SENSA and we showed initial, promising results for it when compared with the predictions from breadth-first traversals of static slices [10], [12]. In this paper, we expanded our presentation of SENSA, its process, algorithm, and modification strategies.…”

Section: Related Workmentioning

confidence: 91%

Prioritizing Change-Impact Analysis via Semantic Program-Dependence Quantification

2016

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Software is constantly changing. To ensure the quality of this process, when preparing to change a program, developers must first identify the main consequences and risks of modifying the program locations they intend to change. This activity is called change-impact analysis. However, existing impact analysis suffers from two major problems: coarse granularity and large size of the resulting impact sets. Finer-grained analyses such as slicing give more detailed impact sets which, however, are also even larger in size. While various impact-set reduction approaches have been proposed at different levels of granularity, the challenge persists as very-large impact sets are still produced, impeding the adoption of impact analysis due to the great costs of inspecting those impact sets. To address these challenges, we present a novel dynamic-analysis technique called SENSA which combines sensitivity analysis and execution differencing. SENSA not only provides finegrained (statement-level) impact sets but also prioritizes potential impacts via semantic-dependence quantification for program slices. We evaluated the benefits of impact prioritization using SENSA with respect to static and dynamic forward slicing via an extensive empirical study of open-source Java applications and three case studies. Our results show that SENSA can offer much better cost-effectiveness than slicing in assisting developers with impact inspection and fault cause-effect understanding.

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Section: Related Workmentioning

confidence: 91%

Prioritizing Change-Impact Analysis via Semantic Program-Dependence Quantification

2016

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“…In preliminary work [31], we described at a high level an early version of SENSA and we showed initial, promising results for it when compared with the predictions from breadth-first traversals of static slices [33,38]. In this paper, we expand our presentation of SENSA, its process, algorithm, and modification strategies.…”

Section: Related Workmentioning

confidence: 97%

SENSA: Sensitivity Analysis for Quantitative Change-Impact Prediction

Cai

Jiang

Santelices

et al. 2014

2014 IEEE 14th International Working Conference on Source Code Analysis and Manipulation

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Sensitivity analysis is used in many fields to determine how different parts of a system respond to variations in stimuli. Softwaredevelopment tasks, such as change-impact analysis, can also benefit from sensitivity analysis. In this paper, we present SENSA, a novel dynamic-analysis technique and tool that uses sensitivity analysis and execution differencing for estimating the influence of program statements on the rest of the program. SENSA not only identifies the statements that can be affected by another statement, but also quantifies those effects. Quantifying effects in programs can greatly increase the effectiveness of tasks such as change-impact analysis by helping developers prioritize and focus their inspection of those effects. Our studies on four Java subjects indicate that SENSA can predict the impact of changes much more accurately than the state of the art: forward slicing. The SENSA prototype tool is freely available to the community for download.

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“…Baah et al [2] propose the probabilistic program dependence graph that composes statistical dependencies deduced from test runs to finally enable improved fault localization and comprehension. Santelices et al [17] propose quantitative slicing that weights statements in a static forward slice using differential executions to improve change impact analysis. Deng and Jones [5] propose the weighted system dependence graph that weights the edges of the system dependence graph using test execution data to better enable debugging and program comprehension.…”

Section: Related Workmentioning

confidence: 99%

Discriminating influences among instructions in a dynamic slice

Palepu

Jones

2014

Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

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Dynamic slicing is an analysis that operates on program execution models (e.g., dynamic dependence graphs) to support the interpreation of program-execution traces. Given an execution event of interest (i.e., the slicing criterion), it solves for all instruction-execution events that either affect or are affected by that slicing criterion, and thereby reduces the search space to find influences within execution traces. Unfortunately, the resulting dynamic slices are still often prohibitively large for many uses. Despite this reduction search space, the dynamic slices are often still prohibitively large for many uses, and moreover, are provided without guidance of which and to what degree those influences are exerted. In this work, we present a novel approach to quantify the relevance of each instruction-execution event within a dynamic slice by its degree of relative influence on the slicing criterion. As such, we augment the dynamic slice with dynamic-relevance measures for each event in the slice, which can be used to guide and prioritize inspection of the events in the slice. We conducted an experiment that evaluates the ability of existing dynamic slicing and our approach, using dynamic relevance, to correctly identify sources of execution influence and state propagation. The results of the experiment show that inspections that were guided by traditional dynamic slicing to find the root cause for a failure reduced the search space by, on average, 61.3%. Further, inspections guided with the assistance of the new dynamic relevance reduced the search space by 96.2%.

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Quantitative program slicing: Separating statements by relevance

Cited by 14 publications

References 14 publications

Prioritizing Change-Impact Analysis via Semantic Program-Dependence Quantification

Prioritizing Change-Impact Analysis via Semantic Program-Dependence Quantification

SENSA: Sensitivity Analysis for Quantitative Change-Impact Prediction

Discriminating influences among instructions in a dynamic slice

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