Comparison of different impact analysis methods and programmer's opinion

Software change impact analysis (CIA) methods enable developers to understand potential impacts of a code change so that the change can be executed confidently without affecting reliability of the software. However, existing CIA approaches do not support CIA for all source code granularities. Additionally, they lack support for inter-granular change impact queries and hidden dependencies. This paper introduces Augur, an automated static code analysis-based CIA approach that addresses these shortcomings. Augur infers and maintains semantic and environment dependencies along with data and control dependencies between source code entities across granularities. Additionally, Augur uses Change Impact Query Language, a novel query language for impact analysis proposed in this paper, to support inter-granular CIA queries with batch querying feature. Augur has been realized as a Visual Studio extension called Augur-Tool. We have conducted quantitative evaluation on two open-source and two industrial projects to assess the accuracy of the tool. Results from the evaluation indicate that Augur provides CIA with high accuracy (average precision 55% and average recall 85%).

Safety standards prescribe change impact analysis (CIA) during evolution of safety-critical software systems. Although CIA is a fundamental activity, there is a lack of empirical studies about how it is performed in practice. We present a case study on CIA in the context of an evolving automation system, based on 14 interviews in Sweden and India. Our analysis suggests that engineers on average spend 50–100 h on CIA per year, but the effort varies considerably with the phases of projects. Also, the respondents presented different connotations to CIA and perceived the importance of CIA differently. We report the most pressing CIA challenges, and several ideas on how to support future CIA. However, we show that measuring the effect of such improvement solutions is non-trivial, as CIA is intertwined with other development activities. While this paper only reports preliminary results, our work contributes empirical insights into practical CIA.

"Change Impact Analysis" is the process of determining the consequences of a modification to software. In theory, change impact analysis should be done during software maintenance, to make sure changes do not introduce new bugs. Many approaches and techniques are proposed to help programmers do change impact analysis automatically. However, it is still an open question whether and how programmers do change impact analysis. In this paper, we conducted two studies, one in-depth study and one breadth study. For the in-depth study, we recorded videos of nine professional programmers repairing two bugs for two hours. For the breadth study, we surveyed 35 professional programmers using an online system. We found that the programmers in our studies did static change impact analysis before they made changes by using IDE navigational functionalities, and they did dynamic change impact analysis after they made changes by running the programs. We also found that they did not use any change impact analysis tools.

Software change is inevitable in the process of software development. At present, there are a lot of researches on the methods of the change impact analysis, but most of them are aimed at a certain stage of the software development process, and these technologies are not suitable for other stages of software development. In this paper, based on the whole software development process, five kinds of entity dependency types and five types of change are proposed and formalized, and their properties are discussed. This paper is to provide a theoretical basis for the research of rule based change impact analysis in the software development process.

Software maintenance is the most demanding and effort-consuming phase in software development. It has been recognized as being a tedious step in software development process. Two basic activities in software maintenance are the understanding of the system and the assessment of the potential effects of a change. A change to a system, however small, can lead to several accidental effects, which are often not obvious and easy to identify. The main purpose of impact analysis is not only to find the impact set in terms of coding elements but also in terms of effort and resources required for implementing the change so that analysts could analyze the impact of the requested change in terms of budget. The objective of this paper is to find the impact set of the change requested by user or client. By using the impact set we estimate the regression test effort. We illustrate our results with a case study. As the results of this work, we get the impact set having impact element as class names with respect to the requested change, the test suite and the effort required for regression testing after the implementation of the requested change.

Software change impact analysis (CIA) is a key technique to identify the potential ripple effects of the changes to software. Coarse-grained CIA techniques such as file, class and method level techniques often gain less precise change impacts, which are difficult for practical use. Fine-grained CIA techniques, such as slicing, can be used to gain more precise change impacts, but need more time and space cost. In this paper, by combining the features of the coarse-grained technique and the fine-grained technique, a variable-method (VM) correlation-based CIA technique called VM-CIA is proposed to improve the precision of static CIA. First, the VM-CIA technique uses the abstract syntax tree (AST) of program to construct a novel intermediate representation called variable and method triple (VMT), which is used to analyze the correlation between the variables and methods. Second, the VM-CIA technique proposes the single-change impact analysis algorithm and multi-change impact analysis algorithm to compute the impact set based on the VMT representation. In addition, the VM-CIA technique can get a sorted impact set which is more accurate than the existing CIA techniques. The empirical results show that the VM-CIA technique can greatly improve the precision (19%) over traditional the CIA techniques, while at the cost of a little recall (5%). Moreover, the empirical studies also show that the VM-CIA technique predicts a ranked list of potential impact results according to the distance measure, which can greatly facilitate the practical use.

 The paper presents the results of a regional analysis of climate change impacts on Norway spruce growth in the north-western part of Slovakia(Orava region). Radial increment was determined from nine X-tree sample plots established in the forests of natural character in the region. The analysis of PTT radial increment was done on tree disks cut from a height of 1.3 m by measurements of four perpendicular directions corresponding to the cardinal points. It was derived from the tree-ring width measured at breast height (1.3 m) while all the basic principles of tree-ring analyses were observed (transport and borehole treatment, measurements with digital positiometer to the nearest 0.01 mm, synchronisation of the tree-ring diagrams). A dendroclimatic model belongs to the category of empirical models based on the statistical evaluation of empirically derived dependences between the time series of tree-ring parameters and the monthly climatic characteristics. This statistical evaluation is based on a multiple linear regression model. Climatic models were used as basic tools for climatic change prediction. There is a scenario coming from the GCM category, which is derived from the models of Canadian Centre for Climate Modelling and Analysis in Victoria (British Columbia, Canada), used for a solution of this task. It is the latest connected model from the second generation designated CCCM 2000. For the purpose of this study the area averages were modified for the meteorological station Oravská Polhora with the 1951–1980 reference period. The modification includes two climatic characteristics, total monthly precipitation and monthly temperature means. The frequency analysis indicates that 24.4% of trees would react to the assumed climatic change negatively, i.e. by decreasing the increment, and 75.6% of trees would react positively. Most of the reactions are moderately positive. It is to conclude that 14.6% of trees will react to a climatic change significantly in a negative way, the reactions of 34.1% trees are considered to be unchanged and 51.3% of trees should react to the assumed climatic change positively (P = 0.95). It results from the analysis of the climatic change impact that the highest effect on stands situated on the upper forest limit can be expected.

Small changes can have major and nonlocal effects in object oriented languages, due to the extensive use of subtyping and dynamic dispatch. This makes it difficult to understand value flow through a program and complicates life for maintenance programmers. Change impact analysis provides feedback on the semantic impact of a set of program changes.The change impact analysis method presented in this thesis presumes the existence of a suite of regression tests associated with a Java program and access to the original and edited versions of the code. The primary goal of our research is to provide programmers with tool support that can help them understand why a test is suddenly failing after a long editing session by isolating the changes responsible for the failure. The tool analyzes two versions of an application and decomposes their difference into a set of atomic changes. Change impact is then reported in terms of affected tests whose execution behavior may have been modified by the applied changes. For each affected test, it also determines a set of affecting changes that were responsible for the test's modified behavior.The first contribution of this thesis is the demonstration of the utility of the basic change impact analysis framework of [51], by implementing a proof-of-concept prototype, Chianti, and applying it to Daikon, for an experimental validation.The second contribution is the definition and implementation of the dependences between atomic changes. Extensive experiments show that our dependences can help build the intermediate programs automatically in most cases.Another contribution is the heuristics for ranking the atomic changes for fault localization. This thesis proposes a heuristic that ranks method changes that might have affected a failed test, indicating the likelihood that they may have contributed to a test failure. Our results indicate that when a failure is caused by a single method change, our heuristic ranked the failure-inducing change as number 1 or number 2 of all the method changes in 67% of the delegate tests (i.e., representatives of all failing tests).

The documentation of software architecture relations as a kind of traceability information is considered important to help people understand the consequences or ripple-effects of architecture evolution. Traceability information provides a basis for analysing and evaluating software evolution, and consequently, it can be used for tasks like reuse evaluation and improvement throughout the evolution of software. To date, however, none of the published empirical studies on software architecture traceability have examined the validity of these propositions. In this paper, we hypothesize that impact analysis of changes in software architecture can be more efficient when supported by traceability links. To test this hypothesis, we designed two controlled experiments that were conducted to investigate the influence of traceability links on the quantity and quality of retrieved assets during architecture evolution analysis. The results provide statistical evidence that a focus on architecture traceability significantly reduces the quantity of missing and incorrect assets, and increases the overall quality of architecture impact analysis for evolution.

Change impact analysis is generally regarded as a very difficult program comprehension problem. One of the reasons for this is that there is no universal definition for dependency between software artifacts, only algorithms that approximate the dependencies. In the past two decades, different kinds of algorithms have been developed by researchers. But which algorithm is the most suitable in a specific situation, which one finds the relevant dependencies in the best way? What kinds of dependencies are important for the programmers? What kinds of algorithms do they work with? Finding the most relevant dependencies is difficult, and it is essentially a creative mental task. A possible way to answer the above questions is to involve programmers in a survey, and listen to their subjective opinions based on expertise and experience in program comprehension. In this paper, we present the results of our survey on this theme. We wanted to know what the difference was between the results of some well-known algorithms and programmers´ opinions and, in addition, among programmers´ opinions as well. Hence we conducted a case study.

FCA–CIA: An approach of using FCA to support cross-level change impact analysis for object oriented Java programs

We propose a technique for analysis of impact of design changes using dynamic slicing of UML models. For a software architecture specified using UML, we first transform a given model into a graph representation which we have named Model Dependency Graph (MDG). MDG combines information available in the interaction model along with the relevant information available in class model into an integrated model. For a given slicing criterion, our slicing algorithm traverses the constructed MDG to identify the relevant model elements forming the dynamic slice. We use these slices to identify the model changes, and their impact. Our impact analysis methodology is based on using the difference between the dynamic slices computed before and after changes are made to the model. Our methodology's novelty lies in performing impact analysis based on a graph metamodel as against independently processing separate UML models.

Regulatory authorities of e.g. the automotive, avionic, or railway domain pose high requirements concerning safety assurance activities. One activity known from requirements engineering (RE) is requirements traceability. We propose a model-based methodology to facilitate trace link visualization over several levels of abstraction and different viewpoints. This intuitive visualization concept allows to see (i) the artifact importance using the degree and betweenness centrality measure, and (ii) the impact of an artifact change to other artifacts. Based on this, a novel impact analysis algorithm is proposed.

Software change impact analysis plays an important role in controlling software evolution in the maintenance of continuous software development. We developed a tool for change impact analysis, which machine-learns change histories and directly outputs candidates of the components to be modified for a change request. We applied the tool to real project data to evaluate it with two metrics: coverage range ratio and accuracy in the coverage range. The results show that it works well for software projects having many change histories for one source code base.

As the complexity of a software system grows, it becomes increasingly difficult for developers to be aware of all the dependencies that exist between artifacts (e.g., files or methods) of the system. Change impact analysis helps to overcome this problem, as it recommends to a developer relevant source-code artifacts related to her current changes. Association rule mining has shown promise in determining change impact by uncovering relevant patterns in the system's change history. State-of-the-art change impact mining algorithms typically make use of a change history of tens of thousands of transactions. For efficiency, targeted association rule mining focuses on only those transactions potentially relevant to answering a particular query. However, even targeted algorithms must consider the complete set of relevant transactions in the history. This paper presents ATARI, a new adaptive approach to association rule mining that considers a dynamic selection of the relevant transactions. It can be viewed as a further constrained version of targeted association rule mining, in which as few as a single transaction might be considered when determining change impact. Our investigation of adaptive change impact mining empirically studies seven algorithm variants. We show that adaptive algorithms are viable, can be just as applicable as the start-of-the-art complete-history algorithms, and even outperform them for certain queries. However, more important than the direct comparison, our investigation lays necessary groundwork for the future study of adaptive techniques and their application to challenges such as the on-the-fly style of impact analysis that is needed at the GitHub-scale.