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In particular, Korel and Laski intro- paper we perform a preliminary study in which we inves- duced dynamic slicing, which computes slices for a particular tigate whether, and to what extent, a popular automated execution i. More precisely, we selected a set of 34 devel- In subsequent years, different variations of dynamic slicing opers with different degrees of expertise, assigned them two have been proposed in the context of debugging, such as different debugging tasks, and compared their performance critical slices [4], relevant slices [9], data-flow slices [31], and when using a representative automated debugging tool [11] pruned slices [30].
These techniques can considerably reduce and when using standard debugging tools available within the size of slices, and thus potentially improve debugging. In the study, However, the sets of relevant statements identified are often we did not just examine whether developers can find bugs still fairly large, and slicing-based debugging techniques are faster using an automated technique; we also tried to jus- rarely used in practice.
For ex- a different philosophy. These techniques identify potentially ample, we found that the use of an automated tool helped faulty code by observing the characteristics of failing pro- more experienced developers find faults faster in the case of gram executions, and often comparing them to the charac- an easy debugging task, but the same developers received teristics of passing executions.
These approaches differ from no benefit from the use of the tool on a harder task. We one another in the type of information they use to character- also found that most developers, when provided with a list ize executions and statements—path profiles [22], counterex- of ranked statements, do not examine the statements in the amples e.
In general, although such information. Additional work in this area has investi- the results of our study are still preliminary, they provide in- gated the use of clustering techniques to eliminate redundant sight into the behavior of developers during debugging and executions and facilitate fault localization [10, 13, 18, 21]. When using these techniques, this of fault localization and debugging in general. This scarcity of studies limits our un- to date is the empirical study of the Whyline tool [14].
In the rest of this section, we summarize the main behavior. For example, a user can click on part of a pro- empirical studies performed so far in this area. The study did not directly evaluate if programmers on those statements, and could continue the investigation could more effectively debug with slicing.
Instead, program- in this interactive way. Overall, slices were recognized bugs from ArgoUML KLOC , participants that used significantly more often than other fragments, which sug- Whyline were able to complete the task twice as fast than gests that programmers tend to follow the flow of execution participants using only a traditional debugger [15].
In summary, as the brief survey in this section shows, The first study to actually examine whether programmers empirical evidence of the usefulness of many automated de- with a slicing tool could debug more effectively was per- bugging techniques is limited, in the case of slicing, when formed by Weiser and Lyle, but could not find any bene- not completely absent, for most other types of techniques.
In the study, they did not observe any improve- This situation makes it difficult to assess the practical effec- ment when developers debugged a small faulty program tiveness of the techniques proposed in the literature and to LOC using a slicing tool.
In a follow-up study, they changed understand which characteristics of a technique can make it several parameters of the experiment.
First, they used a successful. In this paper, we try to fill this gap by studying smaller program 25 LOC. Second, instead of an interac- a set of developers while debugging real bugs with and with- tive tool, they used a paper printout of the sliced program.
The rest Finally, they used a different slicing technique, called dic- of the paper discusses our study and its results. The 3. In an experiment should outperform developers that do not use the tool. Our with 17 students, Francel and Rugaber found that only four first hypothesis is therefore as follows. The Hypothesis 1 - Programmers who debug with the assis- study also showed that these four developers had a better tance of automated debugging tools will locate bugs faster understanding of the program, in comparison to non-slicers, than programmers who debug code completely by hand.
Other differences were found between the groups, such as the fact that non-slicers We would also expect that an automated tool should be were less careful and less systematic in their inspection. In this case, In a subsequent study, Kusumoto and colleagues selected the tool should give developers an edge over traditional de- six students, provided three of them with a slicing tool, buggers.
We hence formulate a second hypothesis. The study involved finding bugs in 3 small pro- creases with the level of difficulty of the debugging task. No significant difference could be found between the groups in this case, so the researchers performed a simplified version Finally, when considering the class of debugging tech- of the experiment in which they used 6 smaller programs niques based on statement ranking, the central assumption 25—52 LOC and 6 faults.
Based on this assump- of the programs, but not for all of them. This concept is expressed six subjects were provided with an Eclipse plugin that showed by our third hypothesis.
Although no definitive conclusions could be drawn from the study, there was some evidence that novice programmers 3. Our first research question asks how realistic is the assumption that programmers would use a ranked list of statements provided by a tool. The rationale for this ques- tion is that the premise of most evaluations of debugging techniques is that developers investigate statements individ- ually, one at a time, until they find the bug.
This assumption gives a one-to-one mapping between the rank of the faulty statements and the assumed effectiveness of an algorithm. Research Question 1 - How do developers navigate a list of statements ranked by suspiciousness? Do they visit them in order of suspiciousness or go from one statement to the other by following a different order?
Our second research question investigates if a programmer Figure 1: Tetris Task: Identify and fix the cause of can identify a faulty statement just by looking at it. An the abnormal rotation of squares in Tetris. We want to assess how realistic this assumption is. We selected participants from the set of graduate students Research Question 2 - Does perfect bug understanding enrolled in graduate-level software engineering courses at exist?
How much effort is actually involved in inspecting Georgia Tech. As part of their coursework, these students and assessing potentially faulty statements? Overall, we had a total of 34 Our final research question seeks to capture and under- participants, whose backgrounds represented a full range of stand the challenges, in terms of problems and also opportu- experiences; several participants worked in industry for one nities, faced by developers when using automated debugging or more years or even ran their own startup companies tools.
What issues or barri- periences outside of school. As every programmer must per- ers prevent their effective use? Can unexpected, emerging form some debugging, from new hires to seasoned experts, strategies be observed? The description of the failure consisted of the screen- 2-src. The program consists of 2, LOC including shot shown in Figure 1 and the following textual description: comments and blanks.
From our previous experience in run- ning programming experiments [20], popular games are ideal The rotation of a square block causes unusual behavior: subjects, as participants are typically familiar with the be- The square block will rise upwards instead of rotating havior of the games and can readily identify game concepts in place which would have no observable effect.
If in the source code. The failure description, shown in Figure 2, con- and blanks. NanoXML, besides being one of the largest sub- tained a stack trace and a test input causing the failure.
As jects used in evaluations of automated debugging techniques the figure shows, the failure consisted of an XMLParseEx- to date, presents many characteristics that Tetris does not ception that was raised because starting and closing XML have. In this sense, using these two subjects lets us inves- tigate two complementary situations—one where the users 4. First, Taran- for which the rank of the faulty statement has been tula is, like most state-of-the-art debugging techniques, based increased up or decreased down.
Second, a thorough empirical evaluation of Taran- 4. Finally, Tarantula is easy to explain and teach to developers. Participants in group A were instructed gin that provides the users with the ranked linked of state- to use the tool to solve the Tetris task. Conversely, partici- ments that would be produced by Tarantula.
We believe We investigated our Hypothesis 2, and assessed whether that this approach has the twofold advantage of 1 letting participants benefited more from using the tool on harder us investigate our research questions directly, by having the tasks, by giving the experimental groups a second task: fix participants operate on a ranked list of statements, and 2 a fault in NanoXML. In this case, we compared the dif- The plugin, shown in Figure 3, works as follows. First, ference in performance for the groups using the tool for the the user inputs a configuration file for a task by pressing the Tetris and the NanoXML tasks.
If the tool were more effec- load file icon. Once the file is loaded, the plugin displays a tive for harder tasks, the performance gain of participants table with several rows, where each rows shows a statement using the tool for the NanoXML task should be better than and the corresponding file name, line number, and suspi- that of participants using the tool on the Tetris task.
Besides clicking on a statement to jump to Our Hypothesis 3 aims to understand the effects of the it, as discussed above, users can also use a previous and next rank of the faulty statement on task performance. To in- button to navigate through the statements. The difference between the two a set of test inputs. If rank were an important fac- For Tetris, for which no test cases were available, we wrote tor, there should be a decrease in performance for the Tetris a capture-replay system that could record the keys pressed task and an increase in performance for the NanoXML task when playing Tetris and replay them as test cases.
Overall, for group D. To answer Research Question 1 on how programmers use the ranked list of statements provided by the tool, we recorded Table 1: Successful task completion time in minutes a log of the navigation history of the participants that used and seconds for all conditions. To answer Research Question 2 on perfect bug Group Tetris NanoXML understanding, we analyzed the log history to measure the A time between clicking on the faulty statement and complet- B ing the task.
Finally, to answer Research Question 3, we gave the participants a questionnaire that asked them to C describe how they used the tool and report any issues and D experiences they had with it. This difference is not Participants performed the study in either a classroom or statistically significant by a two-tailed t-test. However, as our lab. A week before the experiment, we gave the partic- we noted above, we did observe a significant difference in ipants a chance to install the plugin and test it on a sample a previous experiment comparing these conditions.
For the program. At the start of the experiment, the participants NanoXML task, the average completion time, in minutes were instructed on the general purpose of the study and were and seconds, was for group B tool and for told that they had a total of 30 minutes to complete each group A traditional debugging. These values were also task, after which they should move to the next task.
In to- not significantly different. We therefore split the participants To let the participants familiarize with the failures, we from groups A and B into three groups based on their per- had them first replicate such failures. To measure task com- formance: low, medium, and high performers. The low per- pletion time, we instructed participants to record as their formers were likely novices, as they were not able to complete starting time the time when they begun looking at the code any of the tasks within 30 minutes.
The medium performers and investigating the failure. To measure the correctness of were able to complete at least one of the tasks most often a solution and gain a better understanding of how partic- Tetris , and the high performers could complete both tasks. Once participants were done with their tasks, they was A versus B for Tetris. Quick links. Logout Register. Need help with SmartFoxServer? You didn't find an answer in our documentation?
Please, post your questions here! How can i make it show the letters correctly? Re: israely charecters not showing properly Post by Lapo » Mon Jun 25, am Hi, have you tried printing them in the Flash console to see if they show up correctly?
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