George Mathew

Andrzej Banaszuk, Vladimir A. Fonoberov, Thomas A. Frewen et al.

Development of robust dynamical systems and networks such as autonomous aircraft systems capable of accomplishing complex missions faces challenges due to the dynamically evolving uncertainties coming from model uncertainties, necessity to operate in a hostile cluttered urban environment, and the distributed and dynamic nature of the communication and computation resources. Model-based robust design is difficult because of the complexity of the hybrid dynamic models including continuous vehicle dynamics, the discrete models of computations and communications, and the size of the problem. We will overview recent advances in methodology and tools to model, analyze, and design robust autonomous aerospace systems operating in uncertain environment, with stress on efficient uncertainty quantification and robust design using the case studies of the mission including model-based target tracking and search, and trajectory planning in uncertain urban environment. To show that the methodology is generally applicable to uncertain dynamical systems, we will also show examples of application of the new methods to efficient uncertainty quantification of energy usage in buildings, and stability assessment of interconnected power networks.

George Mathew, Kathryn T. Stolee

As code search permeates most activities in software development,code-to-code search has emerged to support using code as a query and retrieving similar code in the search results. Applications include duplicate code detection for refactoring, patch identification for program repair, and language translation. Existing code-to-code search tools rely on static similarity approaches such as the comparison of tokens and abstract syntax trees (AST) to approximate dynamic behavior, leading to low precision. Most tools do not support cross-language code-to-code search, and those that do, rely on machine learning models that require labeled training data. We present Code-to-Code Search Across Languages (COSAL), a cross-language technique that uses both static and dynamic analyses to identify similar code and does not require a machine learning model. Code snippets are ranked using non-dominated sorting based on code token similarity, structural similarity, and behavioral similarity. We empirically evaluate COSAL on two datasets of 43,146Java and Python files and 55,499 Java files and find that 1) code search based on non-dominated ranking of static and dynamic similarity measures is more effective compared to single or weighted measures; and 2) COSAL has better precision and recall compared to state-of-the-art within-language and cross-language code-to-code search tools. We explore the potential for using COSAL on large open-source repositories and discuss scalability to more languages and similarity metrics, providing a gateway for practical,multi-language code-to-code search.

George Mathew, Chris Parnin, Kathryn T Stolee

Successful cross-language clone detection could enable researchers and developers to create robust language migration tools, facilitate learning additional programming languages once one is mastered, and promote reuse of code snippets over a broader codebase. However, identifying cross-language clones presents special challenges to the clone detection problem. A lack of common underlying representation between arbitrary languages means detecting clones requires one of the following solutions: 1) a static analysis framework replicated across each targeted language with annotations matching language features across all languages, or 2) a dynamic analysis framework that detects clones based on runtime behavior. In this work, we demonstrate the feasibility of the latter solution, a dynamic analysis approach called SLACC for cross-language clone detection. Like prior clone detection techniques, we use input/output behavior to match clones, though we overcome limitations of prior work by amplifying the number of inputs and covering more data types; and as a result, achieve better clusters than prior attempts. Since clusters are generated based on input/output behavior, SLACC supports cross-language clone detection. As an added challenge, we target a static typed language, Java, and a dynamic typed language, Python. Compared to HitoshiIO, a recent clone detection tool for Java, SLACC retrieves 6 times as many clusters and has higher precision (86.7% vs. 30.7%). This is the first work to perform clone detection for dynamic typed languages (precision = 87.3%) and the first to perform clone detection across languages that lack a common underlying representation (precision = 94.1%). It provides a first step towards the larger goal of scalable language migration tools.

Tianpei Xia, Rahul Krishna, Jianfeng Chen et al.

Software analytics has been widely used in software engineering for many tasks such as generating effort estimates for software projects. One of the "black arts" of software analytics is tuning the parameters controlling a data mining algorithm. Such hyperparameter optimization has been widely studied in other software analytics domains (e.g. defect prediction and text mining) but, so far, has not been extensively explored for effort estimation. Accordingly, this paper seeks simple, automatic, effective and fast methods for finding good tunings for automatic software effort estimation. We introduce a hyperparameter optimization architecture called OIL (Optimized Inductive Learning). We test OIL on a wide range of hyperparameter optimizers using data from 945 software projects. After tuning, large improvements in effort estimation accuracy were observed (measured in terms of standardized accuracy). From those results, we recommend using regression trees (CART) tuned by different evolution combine with default analogy-based estimator. This particular combination of learner and optimizers often achieves in a few hours what other optimizers need days to weeks of CPU time to accomplish. An important part of this analysis is its reproducibility and refutability. All our scripts and data are on-line. It is hoped that this paper will prompt and enable much more research on better methods to tune software effort estimators.

Tianpei Xia, Jianfeng Chen, George Mathew et al.

Industrial practitioners now face a bewildering array of possible configurations for effort estimation. How to select the best one for a particular dataset? This paper introduces OIL (short for optimized learning), a novel configuration tool for effort estimation based on differential evolution. When tested on 945 software projects, OIL significantly improved effort estimations, after exploring just a few configurations (just a few dozen). Further OIL's results are far better than two methods in widespread use: estimation-via-analogy and a recent state-of-the-art baseline published at TOSEM'15 by Whigham et al. Given that the computational cost of this approach is so low, and the observed improvements are so large, we conclude that SBSE should be a standard component of software effort estimation.

Vivek Nair, Amritanshu Agrawal, Jianfeng Chen et al.

This paper introduces Data-Driven Search-based Software Engineering (DSE), which combines insights from Mining Software Repositories (MSR) and Search-based Software Engineering (SBSE). While MSR formulates software engineering problems as data mining problems, SBSE reformulates SE problems as optimization problems and use meta-heuristic algorithms to solve them. Both MSR and SBSE share the common goal of providing insights to improve software engineering. The algorithms used in these two areas also have intrinsic relationships. We, therefore, argue that combining these two fields is useful for situations (a) which require learning from a large data source or (b) when optimizers need to know the lay of the land to find better solutions, faster. This paper aims to answer the following three questions: (1) What are the various topics addressed by DSE? (2) What types of data are used by the researchers in this area? (3) What research approaches do researchers use? The paper briefly sets out to act as a practical guide to develop new DSE techniques and also to serve as a teaching resource. This paper also presents a resource (tiny.cc/data-se) for exploring DSE. The resource contains 89 artifacts which are related to DSE, divided into 13 groups such as requirements engineering, software product lines, software processes. All the materials in this repository have been used in recent software engineering papers; i.e., for all this material, there exist baseline results against which researchers can comparatively assess their new ideas.

George Mathew, Tim Menzies, Neil A. Ernst et al.

When Requirements Engineering(RE) models are unreasonably complex, they cannot support efficient decision making. SHORT is a tool to simplify that reasoning by exploiting the "key" decisions within RE models. These "keys" have the property that once values are assigned to them, it is very fast to reason over the remaining decisions. Using these "keys", reasoning about RE models can be greatly SHORTened by focusing stakeholder discussion on just these key decisions. This paper evaluates the SHORT tool on eight complex RE models. We find that the number of keys are typically only 12% of all decisions. Since they are so few in number, keys can be used to reason faster about models. For example, using keys, we can optimize over those models (to achieve the most goals at least cost) two to three orders of magnitude faster than standard methods. Better yet, finding those keys is not difficult: SHORT runs in low order polynomial time and terminates in a few minutes for the largest models.

George Mathew, Tim Menzies, Jairus Hihn

Context: Early size predictions (ESP) can lead to errors in effort predictions for software projects. This problem is particular acute in parametric effort models that give extra weight to size factors (for example, the COCOMO model assumes that effort is exponentially proportional to project size). Objective: To test if effort estimates are crippled by bad ESP. Method: Document inaccuracies in early size estimates. Use those error sizes to determine the implications of those inaccuracies via a Monte Carlo perturbation analysis of effort models and an analysis of the equations used in those effort models. Results: While many projects have errors in ESP of up to +/- 100%, those errors add very little to the overall effort estimate error. Specifically, we find no statistically significant difference in the estimation errors seen after increasing ESP errors from 0 to +/- 100%. An analysis of effort estimation models explains why this is so: the net additional impact of ESP error is relatively small compared to the other sources of error associated with in estimation models. Conclusion: ESP errors effect effort estimates by a relatively minor amount. As soon as a model uses a size estimate and other factors to predict project effort, then ESP errors are not crippling to the process of estimation

Tim Menzies, Ye Yang, George Mathew et al.

Context:More than half the literature on software effort estimation (SEE) focuses on comparisons of new estimation methods. Surprisingly, there are no studies comparing state of the art latest methods with decades-old approaches. Objective:To check if new SEE methods generated better estimates than older methods. Method: Firstly, collect effort estimation methods ranging from "classical" COCOMO (parametric estimation over a pre-determined set of attributes) to "modern" (reasoning via analogy using spectral-based clustering plus instance and feature selection, and a recent "baseline method" proposed in ACM Transactions on Software Engineering).Secondly, catalog the list of objections that lead to the development of post-COCOMO estimation methods.Thirdly, characterize each of those objections as a comparison between newer and older estimation methods.Fourthly, using four COCOMO-style data sets (from 1991, 2000, 2005, 2010) and run those comparisons experiments.Fifthly, compare the performance of the different estimators using a Scott-Knott procedure using (i) the A12 effect size to rule out "small" differences and (ii) a 99% confident bootstrap procedure to check for statistically different groupings of treatments). Results: The major negative results of this paper are that for the COCOMO data sets, nothing we studied did any better than Boehm's original procedure. Conclusions: When COCOMO-style attributes are available, we strongly recommend (i) using that data and (ii) use COCOMO to generate predictions. We say this since the experiments of this paper show that, at least for effort estimation,how data is collected is more important than what learner is applied to that data.

George Mathew, Amritanshu Agrawal, Tim Menzies

This paper explores the structure of research papers in software engineering. Using text mining, we study 35,391 software engineering (SE) papers from 34 leading SE venues over the last 25 years. These venues were divided, nearly evenly, between conferences and journals. An important aspect of this analysis is that it is fully automated and repeatable. To achieve that automation, we used a stable topic modeling technique called LDADE that fully automates parameter tuning in LDA. Using LDADE, we mine 11 topics that represent much of the structure of contemporary SE. The 11 topics presented here should not be "set in stone" as the only topics worthy of study in SE. Rather our goal is to report that (a) text mining methods can detect large scale trends within our community; (b) those topic change with time; so (c) it is important to have automatic agents that can update our understanding of our community whenever new data arrives.

Tuhin Sahai, George Mathew, Amit Surana

Can a dynamical system paint masterpieces such as Da Vinci's Mona Lisa or Monet's Water Lilies? Moreover, can this dynamical system be chaotic in the sense that although the trajectories are sensitive to initial conditions, the same painting is created every time? Setting aside the creative aspect of painting a picture, in this work, we develop a novel algorithm to reproduce paintings and photographs. Combining ideas from ergodic theory and control theory, we construct a chaotic dynamical system with predetermined statistical properties. If one makes the spatial distribution of colors in the picture the target distribution, akin to a human, the algorithm first captures large scale features and then goes on to refine small scale features. Beyond reproducing paintings, this approach is expected to have a wide variety of applications such as uncertainty quantification, sampling for efficient inference in scalable machine learning for big data, and developing effective strategies for search and rescue. In particular, our preliminary studies demonstrate that this algorithm provides significant acceleration and higher accuracy than competing methods for Markov Chain Monte Carlo (MCMC).