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At a high level, robust-ness testing constructs tests of systems or components, … There are other routes to getting less wrong Bayesian models by plotting marginal priors or analytically determining the impact of the prior on the primary credible intervals. And there are those prior and posterior predictive checks. Flexibility. Does including gender as an explanatory variable really mean the analysis has accounted for gender differences? This sort of robustness check—and I’ve done it too—has some real problems. And, sometimes, the intention is not so admirable. How do robust processes offer benefits in the lab? They are a way for authors to step back and say “You may be wondering whether the results depend on whether we define variable x as continuous or discrete. There is one area where I feel robustness analyses need to be used more often than they are: the handling of missing data. And that is well and good. So it is a social process, and it is valuable. Definition: Robustness is defined as the degree to which a system operates correctly in the presence of exceptional inputs or stressful environmental conditions. This may be a valuable insight into how to deal with p-hacking, forking paths, and the other statistical problems in modern research. But which assumptions and how many are rarely specified. ‘My pet peeve here is that the robustness checks almost invariably lead to results termed “qualitatively similar.” That in turn is of course code for “not nearly as striking as the result I’m pushing, but with the same sign on the important variable.”’ robustness, robustness test cases generation, automated tools for rob ustness testing, and the asse ssment o f t he sys tem rob ustness metric b y usin g the pass/fail robustnes s test case results. Robust regression is an alternative to least squares regression when data is contaminated with outliers or influential observations and it can also be used for the purpose of detecting influential observations. A common exercise in empirical studies is a “robustness check”, where the researcher examines how certain “core” regression coefficient estimates behave when the regression specification is modified by adding or removing regressors. Discussion of robustness is one way that dispersed wisdom is brought to bear on a paper’s analysis. I am currently a doctoral student in economics in France, I’ve been reading your blog for awhile and I have this question that’s bugging me. Sometimes this makes sense. My impression is that the contributors to this blog’s discussions include a lot of gray hairs, a lot of upstarts, and a lot of cranky iconoclasts. This tutorial is designed for software professionals interested in learning the features of TestNG Framework in simple and easy steps and implement it in practice. and influential … I think that’s a worthwhile project. What you’re worried about in these terms is the analogue of non-hyperbolic fixed points in differential equations: those that have qualitative (dramatic) changes in properties for small changes in the model etc. 1. You do the robustness check and you find that your result persists. I think it’s crucial, whenever the search is on for some putatively general effect, to examine all relevant subsamples. That is, p-values are a sort of measure of robustness across potential samples, under the assumption that the dispersion of the underlying population is accurately reflected in the sample at hand. 2. It’s all a matter of degree; the point, as is often made here, is to model uncertainty, not dispel it. My pet peeve here is that the robustness checks almost invariably lead to results termed “qualitatively similar.” That in turn is of course code for “not nearly as striking as the result I’m pushing, but with the same sign on the important variable.” Then the *really* “qualitatively similar” results don’t even have the results published in a table — the academic equivalent of “Don’t look over there. That a statistical analysis is not robust with respect to the framing of the model should mean roughly that small changes in the inputs cause large changes in the outputs. In statistics, the term robust or robustness refers to the strength of a statistical model, tests, and procedures according to the specific conditions of the statistical analysis a study hopes to achieve.Given that these conditions of a study are met, the models can be verified to be true through the use of mathematical … Reusability The variability of the effect across these cuts is an important part of the story; if its pattern is problematic, that’s a strike against the effect, or its generality at least. The results will apply as a class to a wide range of software components. Demonstrating a result holds after changes to modeling assumptions (the example Andrew describes). The unstable and stable equilibria of a classical circular pendulum are qualitatively different in a fundamental way. I understand conclusions to be what is formed based on the whole of theory, methods, data and analysis, so obviously the results of robustness checks would factor into them. 2. I think this would often be better than specifying a different prior that may not be that different in important ways. The elasticity of the term “qualitatively similar” is such that I once remarked that the similar quality was that both estimates were points in R^n. I like robustness checks that act as a sort of internal replication (i.e. At least in clinical research most journals have such short limits on article length that it is difficult to get an adequate description of even the primary methods and results in. Yet many people with papers that have very weak inferences that struggle with alternative arguments (i.e., have huge endogeneity problems, might have causation backwards, etc) often try to just push the discussions of those weaknesses into an appendix, or a footnote, so that they can be quickly waved away as a robustness test. Similarly, replacing the detector module with a second identical unit had no significant effect on analytical performance. Maybe a different way to put it is that the authors we’re talking about have two motives, to sell their hypotheses and display their methodological peacock feathers. 2 CMU/SEI-2005-TN-015. (Yes, the null is a problematic benchmark, but a t-stat does tell you something of value.). As with all epiphanies of the it-all-comes-down-to sort, I may be shoehorning concepts that are better left apart. Of course the difficult thing is giving operational meaning to the words small and large, and, concomitantly, framing the model in a way sufficiently well-delineated to admit such quantifications (however approximate). The other dimension is what I’m talking about in my above post, which is the motivation for doing a robustness check in the first place. The other names of structural testing includes clear box testing, open box testing, logic driven testing or path driven testing. First, robustness is not binary, although people (especially people with econ training) often talk about it that way. or is there no reason to think that a proportion of the checks will fail? And from this point of view, replication is also about robustness in multiple respects. If it is an observational study, then a result should also be robust to different ways of defining the treatment (e.g. [IEEE Std 24765:2010] Goal: The goal of robustness testing is to develop test cases and test environments where a system's robustness can be assessed. True story: A colleague and I used to joke that our findings were “robust to coding errors” because often we’d find bugs in the little programs we’d written—hey, it happens!—but when we fixed things it just about never changed our main conclusions. Your experience may vary. It is quite common, at least in the circles I travel in, to reflexively apply multiple imputation to analyses where there is missing data. It’s always tough when you’re looking at a press release to figure out what’s going on.”. Robustness testing: Robustness testing is a type of testing that is performed to validate the robustness of the application. (Yes, the null is a … The terms robustness and ruggedness refer to the ability of an analytical method to remain unaffected by small variations in the method parameters (mobile phase composition, column age, column temperature, etc.) However, as technology improved, software became more complex and software projects grew larger. I did, and there’s nothing really interesting.” Of course when the robustness check leads to a sign change, the analysis is no longer a robustness check. Such honest judgments could be very helpful. So, at best, robustness checks “some” assumptions for how they impact the conclusions, and at worst, robustness becomes just another form of the garden of forked paths. Nigerians? These testing points are min-, min, min+, max- and max and max+. Not much is really learned from such an exercise. If you get this wrong who cares about accurate inference ‘given’ this model? I ask this because robustness checks are always just mentioned as a side note to presentations (yes we did a robustness check and it still works!). I don’t think I’ve ever seen a more complex model that disconfirmed the favored hypothesis being chewed out in this way. Third, for me robustness subsumes the sort of testing that has given us p-values and all the rest. This experiment highlights the reliability and robustness that compact, modular instruments can offer laboratories that require workflow flexibility. Robustness testing is any quality assurance methodology focused on testing the robustness of software. No. To some extent, you should also look at “biggest fear” checks, where you simulate data that should break the model and see what the inference does. Drives me nuts as a reviewer when authors describe #2 analyses as “robustness tests”, because it minimizes #2’s (huge) importance (if the goal is causal inference at least). Testing “alternative arguments” — which usually means “alternative mechanisms” for the claimed correlation, attempts to rule out an omitted variable, rule out endogeneity, etc. Another social mechanism is bringing the wisdom of “gray hairs” to bear on an issue. But, there are other, less formal, social mechanisms that might be useful in addressing the problem. From a Bayesian perspective there’s not a huge need for this—to the extent that you have important uncertainty in your assumptions you should incorporate this into your model—but, sure, at the end of the day there are always some data-analysis choices so it can make sense to consider other branches of the multiverse. Robustness testing. Audience This tutorial is designed for software professionals interested in learning the features of TestNG Framework in simple and easy steps and implement it in practice. But generally, the best situation is that, work on modules which take all inputs from a parameter list. 35 years in the business, Keith. In the equation (1), η is the signal to noise ratio, y i is the Quality Function Deviation, problem type “larger-the-better”, which is the case of this application and, n corresponds the number of experiments runs.. In fact, it seems quite efficient. Here one needs a reformulation of the classical hypothesis testing framework that builds such considerations in from the start, but adapted to the logic of data analysis and prediction. When the more complicated model fails to achieve the needed results, it forms an independent test of the unobservable conditions for that model to be more accurate. Adaptable to other products with which it needs interaction. ‘And, the conclusions never change – at least not the conclusions that are reported in the published paper.’ Ignoring it would be like ignoring stability in classical mechanics. Is it not suspicious that I’ve never heard anybody say that their results do NOT pass a check? I think this is related to the commonly used (at least in economics) idea of “these results hold, after accounting for factors X, Y, Z, …). But it’s my impression that robustness checks are typically done to rule out potential objections, not to explore alternatives with an open mind. Unfortunately, upstarts can be co-opted by the currency of prestige into shoring up a flawed structure. This website tends to focus on useful statistical solutions to these problems. large companies have a team with responsibilities to evaluate the developed software in context of the given requirements You can be more or less robust across measurement procedures (apparatuses, proxies, whatever), statistical models (where multiple models are plausible), and—especially—subsamples. If required should be easy to divide into different modules for testing. In those cases I usually don’t even bother to check ‘strikingness’ for the robustness check, just consistency and have in the past strenuously and successfully argued in favour of making the less striking but accessible analysis the one in the main paper. But to be naive, the method also has to employ a leaner model so that the difference can be chalked up to the necessary bells and whistles. > Shouldn’t a Bayesian be doing this too? It can be useful to have someone with deep knowledge of the field share their wisdom about what is real and what is bogus in a given field. [9]The goal of the Ballista is to test the robustness of the existing components. It is a ”black box” testing. So if it is an experiment, the result should be robust to different ways of measuring the same thing (i.e. The system should be easy to test and find defects. I find them used as such. The term "robustness testing… Maybe what is needed are cranky iconoclasts who derive pleasure from smashing idols and are not co-opted by prestige. +1 on both points. This seems to be more effective. If you continue browsing the site, you agree to … While performing the manual testing on any application, we do not need any specific knowledge of any testing tool, rather than have a proper understanding of the product so we can easily prepare the test document. ), I’ve also encountered “robust” used in a third way: For example, if a study about “people” used data from Americans, would the results be the same of the data were from Canadians? Manual testing can be further divided into three types of testing, which are as follows: White box testing ; Black box testing The goal of software testing metrics is to improve the efficiency and effectiveness in the software testing process and to help make better decisions for further testing process by providing reliable data about the testing … In the latter category, robustness testing describes a class of approaches that evaluates the degree to which a sys-tem or component can function correctly in the presence of invalid inputs or stressful environmental conditions. I was wondering if you could shed light on robustness checks, what is their link with replicability? It’s now the cause for an extended couple of paragraphs of why that isn’t the right way to do the problem, and it moves from the robustness checks at the end of the paper to the introduction where it can be safely called the “naive method.”. Structural testing, also known as glass box testing or white box testing is an approach where the tests are derived from the knowledge of the software's structure or internal implementation. Ideally one would include models that are intentionally extreme enough to revise the conclusions of the original analysis, so that one has a sense of just how sensitive the conclusions are to the mysteries of missing data. Third, for me robustness subsumes the sort of testing that has given us p-values and all the rest. In field areas where there are high levels of agreement on appropriate methods and measurement, robustness testing need not be very broad. It’s typically performed under the assumption that whatever you’re doing is just fine, and the audience for the robustness check includes the journal editor, referees, and anyone else out there who might be skeptical of your claims. 1 is for nominal. Other times, though, I suspect that robustness checks lull people into a false sense of you-know-what. test mix. Another social mechanism is calling on the energy of upstarts in a field to challenge existing structures. Robustness can encompass many areas of computer science, such as robust programming, robust machine learning, and Robust Security Network.Formal techniques, such as fuzz testing, are essential to showing robustness since this type of testing … I like the analogy between the data generation process and the model generation process (where ‘the model’ also includes choices about editing data before analysis). . It is the journals that force important information into appendices; it is not something that authors want to do, at least in my experience. And, the conclusions never change – at least not the conclusions that are reported in the published paper. Should be flexible enough to modify. As you are going to use TestNG to handle all levels of Java project testing, it will be helpful if you have a prior knowledge of software development and software testing processes. On the other hand, a test with fewer assumptions is more robust. Is there any theory on what percent of results should pass the robustness check? Yes, I’ve seen this many times. It’s a bit of the Armstrong principle, actually: You do the robustness check to shut up the damn reviewers, you have every motivation for the robustness check to show that your result persists . It is not in the rather common case where the robustness check involves logarithmic transformations (or logistic regressions) of variables whose untransformed units are readily accessible. Robustness testing has also been used to describe the process of verifying the robustness of test cases in a test process. I get what you’re saying, but robustness is in many ways a qualitative concept eg structural stability in the theory of differential equations. is there something shady going on? I often go to seminars where speakers present their statistical evidence for various theses. In both cases, if there is an justifiable ad-hoc adjustment, like data-exclusion, then it is reassuring if the result remains with and without exclusion (better if it’s even bigger). A pretty direct analogy is to the case of having a singular Fisher information matrix at the ML estimate. If I have this wrong I should find out soon, before I teach again…. Software Testing Metrics are the quantitative measures used to estimate the progress, quality, productivity and health of the software testing process. For example, maybe you have discrete data with many categories, you fit using a continuous regression model which makes your analysis easier to perform, more flexible, and also easier to understand and explain—and then it makes sense to do a robustness check, re-fitting using ordered logit, just to check that nothing changes much. But it isn’t intended to be. Example 1: Jackknife Robustness Test The jackknife robustness test is a structured permutation test that systematically excludes one or more observations from the estimation at a time until all observations have been excluded once. Machine learning is a sort of subsample robustness, yes? Downloadable (with restrictions)! People use this term to mean so many different things. Test approach has two techniques: Proactive - An approach in which the test design process is initiated as early as possible in order to find and fix the defects before the build is created. Those types of additional analyses are often absolutely fundamental to the validity of the paper’s core thesis, while robustness tests of the type #1 often are frivolous attempts to head off nagging reviewer comments, just as Andrew describes. In this part of the course, the robustness and ruggedness are introduced and explained.. 49. The more assumptions a test makes, the less robust it is, because all these assumptions must be met for the test to be valid. Robustness testing is known by many different names. Correct. Many of these terms are defined below. Figure 4 displays the results of a robustness test, with the top temperature (TS-Data) occasionally falling below the minimum limit (TVL-Lim).The bottom temperature (BS-Data) from the plant data can be higher or lower than its reference temperature (BS-Ref). Anyway that was my sense for why Andrew made this statement – “From a Bayesian perspective there’s not a huge need for this”. Narrow robustness reports just a handful of alternative specifications, while wide robustness concedes uncertainty among many details of the model. but also (in observational papers at least): The idea is as Andrew states – to make sure your conclusions hold under different assumptions. This doesn’t seem particularly nefarious to me. “Naive” pretty much always means “less techie”. Formalizing what is meant by robustness seems fundamental. This should give you an idea of how successful the robust regression was.Best wishes. Second, robustness has not, to my knowledge, been given the sort of definition that could standardize its methods or measurement. I never said that robustness checks are nefarious. However, whil the analogy with physical stability is useful as a starting point, it does not seem to be useful in guiding the formulation of the relevant definitions (I think this is a point where many approaches go astray). Unfortunately, a field’s “gray hairs” often have the strongest incentives to render bogus judgments because they are so invested in maintaining the structure they built. I don’t know. Vulnerability testing: Vulnerability testing is the process of identifying the vulnerabilities or weaknesses in the application. Mexicans? Robustness testing … Statistical Modeling, Causal Inference, and Social Science. Vulnerability Testing - checklist: Verify the strength of the password as it provides some degree of security. Among other things, Leamer shows that regressions using different sets of control variables, both of which might be deemed reasonable, can lead to different substantive interpretations (see Section V.). keeping the data set fixed). Adhoc testing: Ad-hoc testing is quite opposite to the formal testing… P. If robustness checks were done in an open sprit of exploration, that would be fine. Sensitivity to input parameters is fine, if those input parameters represent real information that you want to include in your model it’s not so fine if the input parameters are arbitrary. Perhaps not quite the same as the specific question, but Hampel once called robust statistics the stability theory of statistics and gave an analogy to stability of differential equations. Or, essentially, model specification. There is probably a Nobel Prize in it if you can shed some which social mechanisms work and when they work and don’t work. measures one should expect to be positively or negatively correlated with the underlying construct you claim to be measuring). This tutorial provides a good understanding on TestNG framework needed to test an enterprise-level application to deliver it with robustness and reliability. In many papers, “robustness test” simultaneously refers to: TestNG is designed to cover all categories of tests: unit, functional, end-to-end, integration, etc., and it requires JDK 5 or higher. I only meant to cast them in a less negative light. I would suggest comparing the residual analysis for the OLS regression with that from the robust regression. Regarding the practice of burying robustness analyses in appendices, I do not blame authors for that. It helps the reader because it gives the current reader the wisdom of previous readers. and so, guess what? I realize its just semantic, but its evidence of serious misplaced emphasis. 48. such software. Test Strategy is also known as test approach defines how testing would be carried out. 6.0 Robustness Testing 8 7.0 Worst Case Testing 9 7.1Robust Worst Case Testing 10 8.0 Examples: Test Cases 12 8.1 Next Date problem 12 8.2 Tri-angle problem 13 9.0 Conclusion 14 10.0 References 15 2. 19= (3*6)+1. Unfortunately as soon as you have non-identifiability, hierarchical models etc these cases can become the norm. 1.0 Introduction The practice of testing software has become one of the most important aspects of the process of … I blame publishers. This sometimes happens in situations where even cursory reflection on the process that generates missingness cannot be called MAR with a straight face. The official reason, as it were, for a robustness check, is to see how your conclusions change when your assumptions change. Of course, there is nothing novel about this point of view, and there has been a lot of work based on it. In computer science, robustness is the ability of a computer system to cope with errors during execution and cope with erroneous input. Robustness checks can serve different goals: 1. windows for regression discontinuity, different ways of instrumenting), robust to what those treatments are bench-marked to (including placebo tests), robust to what you control for…. In both cases, I think the intention is often admirable – it is the execution that falls short. It incorporates social wisdom into the paper and isn’t intended to be statistically rigorous. Is this selection bias? small data sets) – so one had better avoid the mistake made by economists of trying to copy classical mechanics – where it might be profitable to look for ideas, and this has of course been done, is statistical mechanics). If the reason you’re doing it is to buttress a conclusion you already believe, to respond to referees in a way that will allow you to keep your substantive conclusions unchanged, then all sorts of problems can arise. It’s better than nothing. NASA interns exploring robustness testing Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. I have no answers to the specific questions, but Leamer (1983) might be useful background reading: http://faculty.smu.edu/millimet/classes/eco7321/papers/leamer.pdf. But really we see this all the time—I’ve done it too—which is to do alternative analysis for the purpose of confirmation, not exploration. In earlier times, software was simple in nature and hence, software development was a simple activity. This usually means that the regression models (or other similar technique) have included variables intending to capture potential confounding factors. Or just an often very accurate picture ;-). True, positive results are probably overreported and some really bad results are probably hidden, but at the same time it’s not unusual to read that results are sensitive to specification, or that the sign and magnitude of an effect are robust, while significance is not or something like that. Or Andrew’s ordered logit example above. Perhaps “nefarious” is too strong. Vulnerability testing, a software testing technique performed to evaluate the quantum of risks involved in the system in order to reduce the probability of the event. There are 6 possible values like min-, min, min+, max-, max and max+. Robust statistics are statistics with good performance for data drawn from a wide range of probability distributions, especially for distributions that are not normal.Robust statistical methods have been developed for many common problems, such as estimating location, scale, and regression parameters.One … You paint an overly bleak picture of statistical methods research and or published justifications given for methods used. In areas where If the coefficients are plausible and robust, this is … There are a total of 3 variables X, Y and Z. One dimension is what you’re saying, that it’s good to understand the sensitivity of conclusions to assumptions. Software development now necessitated the presence of a team, which could prepare detailed plans and designs, carry out testing… Conclusions that are not robust with respect to input parameters should generally be regarded as useless. The S/N ratio can be also understood as the inverse of variance and the maximization of S/N ratio allows reduction of the … Of course these checks can give false re-assurances, if something is truly, and wildly, spurious then it should be expected to be robust to some these these checks (but not all). Well, that occurred to us too, and so we did … and we found it didn’t make a difference, so you don’t have to be concerned about that.” These types of questions naturally occur to authors, reviewers, and seminar participants, and it is helpful for authors to address them. Good question. Is it a statistically rigorous process? Breaks pretty much the same regularity conditions for the usual asymptotic inferences as having a singular jacobian derivative does for the theory of asymptotic stability based on a linearised model. But then robustness applies to all other dimensions of empirical work. 47. TestNG is a testing framework developed in the lines of JUnit and NUnit, however it introduces some new functionalities that make it more powerful and easier to use. Because the problem is with the hypothesis, the problem is not addressed with robustness checks. Should be easy to interface with other standard 3rd party components. (In other words, is it a result about “people” in general, or just about people of specific nationality?). That is, p-values are a sort of measure of robustness across potential samples, under the assumption that the dispersion of the underlying population is accurately reflected in the sample at hand. In this test, the bottom temperature starts below the reference value. Before proceeding with this tutorial, you should have a basic understanding of Java programming language, text editor, and execution of programs, etc. ANSI and IEEE have defined robustness as the degree to which a system or component can function correctly in the presence of invalid inputs or stressful environmental conditions. Eg put an un-modelled change point in a time series. We can generate 19 test cases from both variables X, Y, and Z. Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond, “We’ve got to look at the analyses, the real granular data. etc. the theory of asymptotic stability -> the theory of asymptotic stability of differential equations. The most extreme is the pizzagate guy, where people keep pointing out major errors in his data and analysis, and he keeps saying that his substantive conclusions are unaffected: it’s a big joke. What I said is that it’s a problem to be using a method whose goal is to demonstrate that your main analysis is OK. Yes, as far as I am aware, “robustness” is a vague and loosely used term by economists – used to mean many possible things and motivated for many different reasons. But the usual reason for a robustness check, I think, is to demonstrate that your main analysis is OK. Many of these are equivalent, and some are used to define a specific type of robustness testing. obvious typo at the end: “some of these checks” not “some these these checks”. Economists reacted to that by including robustness checks in their papers, as mentioned in passing on the first page of Angrist and Pischke (2010): I think of robustness checks as FAQs, i.e, responses to questions the reader may be having. However, robustness generally comes at the cost of power, because either less information from the input is used, or more … Robustness checks involve reporting alternative specifications that test the same hypothesis. Funnily enough both have more advanced theories of stability for these cases based on algebraic topology and singularity theory. With a group-wise jackknife robustness test, researchers systematically drop a set of Expediting organised experience: What statistics should be? Also, the point of the robustness check is not to offer a whole new perspective, but to increase or decrease confidence in a particular finding/analysis. But on the second: Wider (routine) adoption of online supplements (and linking to them in the body of the article’s online form) seems to be a reasonable solution to article length limits. In situations where missingness is plausibly strongly related to the unobserved values, and nothing that has been observed will straighten this out through conditioning, a reasonable approach is to develop several different models of the missing data and apply them. As discussed frequently on this blog, this “accounting” is usually vague and loosely used. Ad hoc testing: a testing phase where the tester tries to "break" the system by randomly This tutorial provides a good understanding on TestNG framework needed to test an enterprise-level application to deliver it with robustness and reliability. . (To put an example: much of physics focuss on near equilibrium problems, and stability can be described very airily as tending to return towards equilibrium, or not escaping from it – in statistics there is no obvious corresponding notion of equilibrium and to the extent that there is (maybe long term asymptotic behavior is somehow grossly analogous) a lot of the interesting problems are far from equilibrium (e.g. (I’m a political scientist if that helps interpret this. It’s interesting this topic has come up; I’ve begun to think a lot in terms of robustness.

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