Dynamic technology impact analysis: A multi-task learning approach to patent citation prediction

Dynamic impact analysis based on whole path profiling of method calls and returns has been shown to provide more useful predictions of software change impacts than method-level static slicing and to avoid the overhead of expensive dependency analysis needed for dynamic slicing-based impact analysis. This work presents the design, implementation, and evaluation of an online approach to dynamic impact analysis as an extension to the DynamoRIO binary code modification system and to the Jikes Research Virtual Machine. Storage and postmortem analysis of program traces, even compressed, are avoided.

Dynamic impact analysis produces more precise results than static impact analysis [1]. However, existing dynamic impact analysis techniques [2,3,4] do not consider the differences of object-oriented (OO) programs from procedural programs. We introduced a dynamic impact analysis approach [5] for OO programs that considers the unique features of OO programs compared to procedural ones and the dependency relationships of OO runtime program entities. In this paper, we present the further improved approach with the ability to identify runtime OO inheritance relationships. Furthermore, we present the implementation of our approach - a tool named Javatrade dynamic impact analyzer (JDIA) that can perform dynamic impact analysis for Java programs residing in a local or a remote Java virtual machine (JVM). Empirical studies of JDIA are presented to show that our approach produces more precise results than the existing dynamic impact analysis techniques by performing dependency analysis.

Impact analysis determines the effects that program entities of interest, or changes to them, may have on the rest of the program for software measurement, maintenance, and evolution tasks. Dynamic impact analysis could be one major approach to impact analysis that computes smaller impact setsthan static alternatives for concrete sets of executions. However, existing dynamic approaches often produce impact sets that are too large to be useful, hindering their adoption in practice. To address this problem, we propose to exploit static program dependencies to drastically prune false-positive impacts that are not exercised by the set of executions utilized by the analysis, via hybrid dependence approximation. Further, we present a novel dynamic impact analysis called Diver which leverages both the information provided by the dependence graph and method-execution events to identify runtimemethod-level dependencies, hence dynamic impact sets, much more precisely without reducing safety and at acceptable costs. We evaluate Diver on ten Java subjects of various sizes and application domains against both arbitrary queries covering entire programs and practical queries based on changes actually committed by developers to actively evolving software repositories. Our extensive empirical studies show that Diver can significantly improve the precision of impact prediction, with 100-186 percent increase, with respect to a representative existing alternative thus provide a far more effective option for dynamic impact prediction. Following a similar rationale to Diver, we further developed and evaluated an online dynamic impact analysis called DiverOnline which produces impact sets immediately upon the termination of program execution. Our results show that compared to the offline approach, for the same precision, the online approach can reduce the time by 50 percent on average for answering all possible queries in the given program at once albeit at the price of possibly significant increase in runtime overhead. For users interested in one specific query only, the online approach may compute the impact set for that query during runtime without much slowing down normal program operation. Further, the online analysis, which does not incur any space cost beyond the static-analysis phase, may be favored against the offline approach when trace storage and/or related file-system resource consumption becomes a serious challenge or even stopper for adopting dynamic impact prediction. Therefore, the online and offline analysis together offer complementary options to practitioners accommodating varied application/task scenarios and diverse budget constraints.

Impact analysis as a critical step in software evolution assists developers with decision making as regards whether and where to apply code changes in evolving software. Dynamic approaches to this analysis particularly focus on the effects of potential code changes to a program with respect to its concrete executions. Given the existence of a number of prior approaches to dynamic impact analysis as opposed to a lack of systematic understanding of their performance, the first comprehensive study of the predictive accuracy of dynamic impact analysis was conducted, comparing the performance of representative techniques in this area against various kinds of realized code changes. This paper reflects on the progress in dynamic impact analysis, concerning the impact of that earlier study on later research. We also situate dynamic impact analysis within the current research and practice on impact analysis in general, and envision relevant future research vectors in this area.

Dynamic impact analysis of the grid structure using multi-point constraint (MPC) equation under the lateral impact load

Dynamic impact analysis based on program executions has shown promise in aiding tasks in the life cycle of large-scale systems. Dynamic impact analysis techniques have shown to produce more precise results than static impact analysis [1]. However, current dynamic impact analysis techniques lack important features such as analysis of dependency among program entities and consideration of object-oriented programs ' features. Thus they may produce imprecise results. In this paper, we present a precise dynamic impact analysis approach for object-oriented programs. This approach considers the characteristics of object- oriented programs and performs dependency analysis which may potentially reduce the impact sets by eliminating elements that do not have dependency on the changed elements. Even though our discussion in this paper is based on Java <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> programming language, this approach can be carried out in a language independent manner for broader applications.

A comprehensive study of the predictive accuracy of dynamic change-impact analysis

The reliability and security of software are affected by its constant changes. For that reason, developers use change-impact analysis early to identify the potential consequences of changing a program location. Dynamic impact analysis, in particular, identifies potential impacts on concrete, typical executions. However, the accuracy (precision and recall) of dynamic impact analyses for predicting the actual impacts of changes has not been studied. In this paper, we present a novel approach based on sensitivity analysis and execution differencing to estimate, for the first time, the accuracy of dynamic impact analyses. Unlike approaches that only use software repositories, which might not be available or might contain insufficient changes, our approach makes changes to every part of the software to identify actually impacted code and compare it with the predictions of dynamic impact analysis. Using this approach in addition to changes made by other researchers on multiple Java subjects, we estimated the accuracy of the best method-level dynamic impact analysis in the literature. Our results suggest that dynamic impact analysis can be surprisingly inaccurate with an average precision of 47-52% and recall of 56-87%. This study offers insights to developers into the effectiveness of existing dynamic impact analyses and motivates the future development of more accurate analyses.

Software change impact analysis is the process of determining the potential effects, or impacts, of a change to a program. Strategies for impact analysis vary in their approach toward the opposing goals of high precision and low analysis time. Fine-grained techniques, such as slicing, can be used to gain very precise knowledge of a change's impact, but may be prohibitively expensive. Coarse-grained techniques such as method-level impact analyses sacrifice precision for faster analysis. In this paper, we present static and dynamic method-level impact analysis algorithms that utilize value propagation information from the source code to increase precision and keep analysis times low. We experimentally compare the results of our analyses with common static and dynamic impact analysis techniques. Our results show that the precision of the common method-level analyses can be improved with very little added overhead

This paper introduces dynamic impact analysis as a cost-effective technique to enforce the error-propagation condition for detecting a fault. The intuition behind dynamic impact analysis is as follows. In a specific test-case, if an execution of a syntactic component has a strong impact on the program output and if the output is correct, then the value of that component-execution is not likely to be erroneous. To capture this intuition in a theoretical framework the notion of impact is formally defined and the concept of impact strength is proposed as a quantitative measure of the impact. In order to provide an infrastructure supporting the computation of impact strengths, program impact graphs and execution impact graphs are introduced. An empirical study validating the computation of impact strengths is presented. It is shown that the impact strengths computed by dynamic impact analysis provide reasonable estimates for the error-sensitivity with respect to the output except when the impact is via one or more error-tolerant components of the program. Potential applications of dynamic impact analysis in the area of mutation testing and dynamic program slicing are discussed.

This study presents the experimental, numerical analysis, and dynamic impact analysis of a building collapse caused by a rainfall-induced landslide (vertical cut slope failure) on 15 August 2018, in Peringavu, Kerala, India, which resulted in the death of nine people. The volume of 1500 m3 soil-applied lateral thrust force on the building’s rear side led to its demolition. The study includes extensive geotechnical characterization. General limit equilibrium and finite element methods were used in the numerical analysis. The infiltration analysis involved a rainfall pattern of low, moderate, and higher intensities on the slope. The study involved a two-stage analysis. Firstly, the analysis of the vertical cut slope with the application rainfall intensities, and second, the analysis of the building under the dynamic impact of the landslide. As a result of the study, the failure mechanism of the vertical cut during intense rainfall and triggering factors were evaluated. The dynamic impact analysis was carried out to examine the effects of the impact of the landslide debris on the building and the performance of the building under the impact load. The load-bearing walls experienced high-intensity impact force developed by the landslide, resulting in the lateral displacement of 170 mm and differential settlement of 92 mm, which led to the building’s demolition. The flexural failures, excessive deflections, bending moments, foundation settlements, and displacement of structural elements were studied.

Impact analysis determines the effects that the behavior of program entities, or changes to them, can have on the rest of the system. Dynamic impact analysis is one practical form that computes smaller impact sets than static alternatives for concrete sets of executions. However, existing dynamic approaches can still produce impact sets that are too large to be useful. To address this problem, we present a novel dynamic impact analysis called DIVER that exploits static dependencies to identify runtime impacts much more precisely without reducing safety and at acceptable costs. Our preliminary empirical evaluation shows that DIVER can significantly increase the precision of dynamic impact analysis.

Early warning and intelligent decisions have proved to be important tools to handle the unprecedented events (wildcards) that might emerge in the future. Relying on forecasting techniques only are not enough to shape the future, since they depend only on the historical shape and they generate one image of the future. The Futures Methodologies are capable of overcoming the constraints imposed on the Forecasting techniques. This is so since they explore, create, and test both possible and desirable futures to improve the decision making process and combine quantitative and qualitative techniques. Cross Impact Analysis generates occurrence probabilities of wildcards taking into account the interdependencies between their occurrences at a specific point in the future. Cross Impact Analysis is a hybrid quantitative and qualitative futures methodology and is very prominent in the Futures Studies literature. This paper introduces a novel contribution to the Futures Studies literature. The Dynamic Cross Impact Analysis is an enhancement to the traditional Cross Impact Analysis by adding the dynamic behavior, time dimension, through the use of Markov Chains. It generates dependent wildcards occurrence probabilities for a number of future years. As a result of this hybridization, a more realistic and rational anticipation of the future is obtained and hence allows for better decision making. The proposed hybrid methodology is applied to the tourism sector to study different wildcards.

The objective of this research is to propose the methodology that could predict the dynamic failure behaviour on the grid structure for the PWR(Pressurized Water Reactor) fuel assembly. To perform this objective, the two kinds of approach are used in this work. First, in order to obtain the test data on the dynamic failure behaviour of the spacer grid, the impact test is performed with the 5×5 cell size partial grid specimen, which is made of Zircaloy-4 thin plate. Second, a finite element analysis method for predicting the buckling behaviour on the spacer grid structure is established by a commercial finite element code ABAQUS/Explicit. In this FE analysis method, appropriate boundary conditions and impact loading conditions are applied to simulate the actual test conditions. The dynamic impact analysis is performed for predicting the buckling behaviour of a grid structure under the lateral impact load by finite element method. The grid structure is composed of several thin plates which were inserted each other and then each contact places were welded by laser welding. The finite element model is produced using pre-processor I-DEAS, and solved using nonlinear commercial solver ABAQUS/explicit. In this work, two models are proposed for FE analysis. One is the simplified model and the other is multi constrained one. Applied boundary conditions for dynamic impact analysis were nearly the same with actual boundary conditions for impact test. The dynamic impact parameters of a grid structure such as critical impact acceleration, impact force and buckling mode and so on, are over-estimated with test results. Based on these results, the FE analysis model of the grid structure is needed to modify the stiffness of a grid cell structure. According to these results, the FE model is modified for estimating the buckling behaviour of grid structure. This modified FE model will be compared with test and analysis results using the simplified model. In addition, the finite element analysis model is in good agreement with the impact test results, therefore this finite element model and the analysis procedure will be used as a good tool for predicting the dynamic buckling behaviour of the grid structure.

Impact analysis not only assists developers with change planning and management, but also facilitates a range of other client analyses, such as testing and debugging. In particular, for developers working in the context of specific program executions, dynamic impact analysis is usually more desirable than static approaches, as it produces more manageable and relevant results with respect to those concrete executions. However, existing techniques for this analysis mostly lie on two extremes: either fast, but too imprecise, or more precise, yet overly expensive. In practice, both more cost-effective techniques and variable cost-effectiveness trade-offs are in demand to fit a variety of usage scenarios and budgets of impact analysis. This article aims to fill the gap between these two extremes with an array of cost-effective analyses and, more broadly, to explore the cost and effectiveness dimensions in the design space of impact analysis. We present the development and evaluation of D ia P ro , a framework that unifies a series of impact analyses, including three new hybrid techniques that combine static and dynamic analyses. Harnessing both static dependencies and multiple forms of dynamic data including method-execution events, statement coverage, and dynamic points-to sets, D ia P ro prunes false-positive impacts with varying strength for variant effectiveness and overheads. The framework also facilitates an in-depth examination of the effects of various program information on the cost-effectiveness of impact analysis. We applied D ia P ro to ten Java applications in diverse scales and domains, evaluating it thoroughly on both arbitrary and repository-based queries from those applications. We show that the three new analyses are all significantly more effective than existing alternatives while remaining efficient, and the D ia P ro framework, as a whole, provides flexible cost-effectiveness choices for impact analysis with the best options for variable needs and budgets. Our study results also suggest that hybrid techniques tend to be much more cost-effective than purely dynamic approaches, in general, and that statement coverage has mostly stronger effects than dynamic points-to sets on the cost-effectiveness of dynamic impact analysis, while static dependencies have even stronger effects than both forms of dynamic data.