Research Paper] The Case for Adaptive Change Recommendation

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%).

Change impact analysis techniques estimate the potential effects of changes made to software. Directed Incremental Symbolic Execution (DiSE) is an intraprocedural technique for characterizing the impact of software changes on program behaviors. DiSE first estimates the impact of the changes on the source code using program slicing techniques, and then uses the impact sets to guide symbolic execution to generate path conditions that characterize impacted program behaviors. DiSE, however, cannot reason about the flow of impact between methods and will fail to generate path conditions for certain impacted program behaviors. In this work, we present iDiSE, an extension to DiSE that performs an interprocedural analysis. iDiSE combines static and dynamic calling context information to efficiently generate impacted program behaviors across calling contexts. Information about impacted program behaviors is useful for testing, verification, and debugging of evolving programs. We present a case-study of our implementation of the iDiSE algorithm to demonstrate its efficiency at computing impacted program behaviors. Traditional notions of coverage are insufficient for characterizing the testing efforts used to validate evolving program behaviors because they do not take into account the impact of changes to the code. In this work we present novel definitions of impacted coverage metrics that are useful for evaluating the testing effort required to test evolving programs. We then describe how the notions of impacted coverage can be used to configure techniques such as DiSE and iDiSE in order to support regression testing related tasks. We also discuss how DiSE and iDiSE can be configured for debugging; finding the root cause of errors introduced by changes made to the code. In our empirical evaluation we demonstrate that the configurations of DiSE and iDiSE can be used to support various software maintenance tasks.

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.

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.

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).

"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.

A systematic Change Impact Analysis (CIA) is being used for better change management of software. Also, CIA process is evolved continuously to make it more effective. Software metrics play an important role to evaluate CIA process. Two types of metrics are used to evaluate CIA. First types of metrics are the standard metrics used to evaluate the performance of CIA techniques for example Precision, Recall, F-measure etc. These are most commonly used by researchers. Second types of metrics are those which are used to quantify the change impact which is based on the code/design features. This paper is aimed at identification of these second types of metrics available in literature.

Large systems often have a long life-span and their system and software architecture design comprise many intricately related elements. The verification and maintenance of these architecture designs require an understanding of how and why the system are constructed. Design rationale is the reasoning behind a design and it provides an explanation of the design. However, the reasoning is often undocumented or unstructured in practice. This causes difficulties in the understanding of the original design, and makes it hard to detect inconsistencies, omissions and conflicts without any explanations to the intricacies of the design. Research into design rationale in the past has focused on argumentation-based design deliberations. Argumentation-based design rationale models provide an explicit representation of design rationale. However, these methods are ineffective in communicating design reasoning in practice because they do not support tracing to design elements and requirements in an effective manner. In this thesis, we firstly report a survey of practising architects to understand their perception of the value of design rationale and how they use and document this knowledge. From the survey, we have discovered that practitioners recognize the importance of documenting design rationale and frequently use them to reason about their design choices. However, they have indicated certain barriers to the use and documentation of design rationale. The results have indicated that there is no systematic approach to using and capturing design rationale in current architecture design practice. Using these findings, we address the issues of representing and applying architecture design rationale. We have constructed a rationale-based architecture model to represent design rationale, design objects and their relationships, which we call Architecture Rationale and Element Linkage (AREL). AREL captures both qualitative and quantitative rationale for architecture design. Quantitative rationale uses costs, benefits and risks to justify architecture decisions. Qualitative rationale documents the issues, arguments, alternatives and tradeoffs of a design decision. With the quantitative and qualitative rationale, the AREL model provides reasoning support to explain why architecture elements exist and what assumptions and constraints they depend on. Using a causal relationship in the AREL model, architecture decisions and architecture elements are linked together to explain the reasoning of the architecture design. Architecture Rationalisation Method (ARM) is a methodology that makes use of AREL to facilitate architecture design. ARM uses cost, benefit and risk as fundamental elements to rank and compare alternative solutions in the decision making process. Using the AREL model, we have proposed traceability and probabilistic techniques based on Bayesian Belief Networks (BBN) to support architecture understanding and maintenance. These techniques can help to carry out change impact analysis and rootcause analysis. The traceability techniques comprise of forward, backward and evolution tracings. Architects can trace the architecture design to discover the change impacts by analysing the qualitative reasons and the relationships in the architecture design. We have integrated BBN to AREL to provide an additional method where probability is used to evaluate and reason about the change impacts in the architecture design. This integration provides quantifiable support to AREL to perform predictive, diagnostic and combined reasoning. In order to align closely with industry practices, we have chosen to represent the rationale-based architecture model in UML. In a case study, the AREL model is applied retrospectively to a real-life bank payment systems to demonstrate its features and applications. Practising architects who are experts in the electronic payment system domain have been invited to evaluate the case study. They have found that AREL is useful in helping them understand the system architecture when they compared AREL with traditional design specifications. They have commented that AREL can be useful to support the verification and maintenance of the architecture because architects do not need to reconstruct or second-guess the design reasoning. We have implemented an AREL tool-set that is comprised of commercially available and custom-developed programs. It enables the capture of architecture design and its design rationale using a commercially available UML tool. It checks the well-formedness of an AREL model. It integrates a commercially available BBN tool to reason about the architecture design and to estimate its change impacts.

As business services become increasingly dependent on information technology (IT), it also becomes increasingly important to maximize the decision support for managing IT. Configuration Management Data Bases (CMDBs) store fundamental information about IT systems, such as the system's hardware, software and services. This information can help provide decision support for root cause analysis and change impact analysis. We have worked with our industrial research partner, CA, and with CA customers to identify challenges to the use of CMDBs to semi-automatically solve these problems. In this paper we propose a framework called DRACA (Decision Support for Root Cause Analysis and Change Impact Analysis). This framework mines key facts from the CMDB and in a sequence of three steps combines these facts with incident reports, change reports and expert knowledge, along with temporal information, to construct a probabilistic causality graph. Root causes are predicted and ranked by probabilistically tracing causality edges backwards from incidents to likely causes. Conversely, change impacts can be predicted and ranked by tracing from a proposed change forward along causality edges to locate likely undesirable impacts.

The main aim of our research project was to determine the extent to which the outflow can be influenced by the human interventions in three-selected water basins in Bohemian Forest (Šumava Mountains) and foothills. The rainfall-runoff analyses using both the single and double mass curves over the period of the hydrologic observations were taken as a basic methodology. Beside mean discharge, precipitation, snow and air temperature trends, analysis of land cover change and human impact on the river network and drainage areas development were applied too. The greatest deviations were widely observed in the period between the 2nd half of the seventies and in the 1st half of the eighties. The whole system came slowly back to its initial condition in the early nineties. The runoff trend deviation has been related to the nature and human factors, mainly to current climatic changes and changes of landscape retention potential.

The motivation of this fundamental research is to reveal the response of ecosystem services to land use change and to support the sustainable development of ecology, economy, and society. Considering the fragile eco-environment, intensive man-land relationship and the world-famous production base of Moutai liquor in China, we select Chishui River Basin (CRB) as the research region. In this study, the InVEST model is used to evaluate the impact of land use change on the spatial pattern of four ecosystem services including water yield, soil retention, carbon storage, and nitrogen export. Meanwhile, the spatial differentiation characteristics, service level, and trade-off using Pearson correlation analysis of each ecosystem service from 2000 to 2020 are explored. The results show the following: (1) The forest increased, while the grassland and cultivated decreased. Especially, the developed has increased greatly from [Formula: see text] to [Formula: see text]. (2) Each ecosystem service has spatio-temporal heterogeneity. The water yield and nitrogen export decreased, while soil retention and carbon storage showed an upward trend. (3) There are great differences in the level of ecosystem services among different land use types. The soil retention and carbon storage of forest are the highest, and the water yield of developed is the highest. (4) There are trade-offs and synergies among ecosystem services, such as the trade-off between water yield and soil retention is increasing, the synergetic relationship between water yield and nitrogen export is decreased. This is important evidence that the forest plays a major role in providing ecosystem services, and the improvement of watershed ecological services generally benefits from the implementation of ecological governance projects in recent years. However, it is worth noting that the frequent changes in land use and the intensification of urbanization have also brought potential risks to the local ecology. Therefore, all our preliminary results throw light on the nature of the impact of land use change on ecology and the trade-off relationship of ecosystem services, which is conducive to scientific protection of watershed ecological environment and the formulation of reasonable land use planning policies.

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.

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.

In change impact analysis, obtaining guidance from automatic tools would be highly desirable since this activity is generally seen as a very difficult program comprehension problem. However, since the notion of an 'impact set' (or dependency set) of a specific change is usually very inexact and context dependent, the approaches and algorithms for computing these sets are also very diverse producing quite different results. The question 'which algorithm finds program dependencies in the most efficient way?' has been preoccupying researchers for a long time, but there are still very few results published on the comparison of the different algorithms to what programmers think are real dependencies. In this work, we report on our experiment conducted with this goal in mind using a compact, easily comprehensible Java experimental software system, simulated program changes, and a group of programmers who were asked to perform impact analysis with the help of different tools and on the basis of their programming experience. We show which algorithms turned out to be the closest to the programmers' opinion in this case study. However, the results also certified that most existing algorithms need to be further enhanced and an effective methodology to use automated tools to support impact analysis still needs to be found.

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