Doctoral Symposium of Masud Rahman

Editor's Notes

• #2: Hello everyone! Good afternoon! My name is Masud Rahman. I am a PhD Candidate from University of Saskatchewan, Canada I work with Dr. Chanchal K. Roy. Today, I will be talking about automated query reformulations for code search.
• #3: Today, my talk will be divided into three sections. In the first section, I will discuss the research problem I am trying to solve in my PhD. In the second section, I will discuss about my PhD Thesis that solves the research problem. Finally, we will have a Q&A session and interesting discussions.
• #4: Part 1: Research Problem
• #5: You are looking at two aircrafts -- Ethiopian airlines and Lion Air Indonesia. These are called the nose-down situation. Due to these nose down situations, we have two fatal crashes in a single calendar year. These crashes took 346 precious human lives and cost trillions of dollars. Now, the culprit is MCAS. This is a software component that was added to Boeing 737-Max 8 version. The bottom line conclusion is, this is a faulty component, not well designed, and ultimately leads to crash. That is why, Boeing 737 Max planes are grounded right now.
• #6: Now, lets say, a Boeing customer has submitted a bug report. Now, a Boeing developer is responsible to locate and repair the faulty code triggering that bug. As a frequent practice, developer chooses a few important keywords and attempts to locate the buggy code within the Boeing codebase. But the study shows that 88% of the keywords chosen by the developer could be incorrect. That is, they do not return the buggy code. So, the obvious next step is to reformulate the query through automated tool supports, so that the buggy code could be located. There are also tools that take a bug report and suggest appropriate search queries in the first place. So, we are interested into these part of the process, and my PhD focuses on this.
• #7: So what we did? We did a systematic literature survey using 56 primary studies on query reformulation for code search. During this study, we found 3 major issues in the literature.
• #8: Now, this is a metric which has been on the play from last the century. It was proposed in the 70s. It is a good metric, but it was actually proposed for regular texts such as news articles. On the other hand, we are dealing with source code here. Now, regular texts and source code have different semantics and different structures. They are not the same So, metrics for regular texts are not appropriate for the source code– this is our hypothesis. So, here is our first research question? How does TF-IDF perform? If not good, can we propose something new?
• #9: We did an empirical study with 5K+ bug reports in our ICSE poster. And we discovered that bug reports could be very different in terms of quality. There could be different types of bug reports. It could be noisy with stack traces which is 16% It could be really poor that does not contain any structured entities, which is 30% Or it could be rich bug reports that include source code, test case and other stuffs, which is 54% Now, what the existing studies do? They treat all these different types of bug reports like the same. So, in their approach, everybody does not get a chance to watch the game. So, here is our second research question. Can we incorporate reporting quality into bug localization and deliver better queries?
• #10: Identifying similar words is very important during query reformulation. We found that WordNet has been extensively used by the literature for finding the similar words. Now, it is good for regular texts. But again, we are dealing with source code here. Evidence suggest that WordNet might not work well for source code. However, those were old days. Now we have Stack Overflow and advanced tools like FastText for semantic similarity calculation. So, here is our third RQ. Can deliver appropriate keywords during code search using Stack Overflow and FastText?
• #11: Now, we are done with Background concepts, Part 1. Now, we are going into Part 2 -- PhD Thesis
• #12: So, this is our thesis statement. We hypothesize that we can improve the query reformulation using graph-based term weighting rather than TF-IDF Bug report quality and document contexts Crowdsourced knowledge, i.e., Stack Overflow and Data analytics such as word embedding from FastText. So, to evaluate these hypothesis, we conduct six studies in the PhD. The first and second study address RQ1, the third study addresses RQ2 and the rest answers RQ3
• #14: Similarly, we can see the phrases and dependencies among the terms in the bug report texts as well. Our job is to identify the keywords from these texts, right? So, did we do? We consider the co-occurrences among the terms. That is, how terms occur with other terms within a certain context. We encode such co-occurrences as edges, and transform the texts into a graph like this.
• #15: Besides term co-occurrences, we consider another aspect called syntactic dependencies. For this, we used Jespersen Rank Theory, a theory developed back in 1925. According this theory, parts of speech of sentence can be divided into three ranks – nouns (first), verbs + adjectives in the second rank and the rest are the third ranks According to Jespersen, verb and adjective modifies noun. That is there are some syntactic dependencies for between element and reported and element and plain to covey the overall meaning of the sentence. Now, we capture such syntactic dependencies as well, and transform the report texts into a POS graph as well.
• #16: So, we have created two graphs, right? Now, we have two graphs developed from the bug report based on two different dimensions --Word co-occurrence and syntactic dependence. Once we have graphs, we apply this famous algorithm called PageRank algorithm. This is the backbone of Google search. Now, the algorithmic details are a bit complex, but I will try to provide an overview here. Why do you think, this guy is laughing? Because, it is getting the maximum votes. Similarly, in the graph, the node that is connected to most of the nodes is the winner. That is, a term’s importance will be determined by its connectivity with other nodes. More importantly, since this is a recursive algorithm, the importance depends on the weights of the connected node as well. Once the computation is done, we get a reformulation candidate from each graph. What is the reformulation candidate? – a ranked list of keywords like this. So, we collect two candidates from two graph, apply machine learning and suggest the best one as our suggested query from the bug report.
• #17: So, first we take a bug report as input. Then we apply regular expressions to identify the structured components. We then classify whether this is a a noisy report containing stack traces. a poor bug report containing only regular texts a rich bug report containing source code and texts. Once the quality level is identified, what’s the next step? Well, we do query reformulation unlike the earlier studies. We separate signals from noise from noisy report, feed the poor bug report with appropriate keywords. We mostly keep the rich bug report as is. So, that is the equity approach.
• #18: First, we construct a semantic hyperspace using Stack Overflow corpus. What is hyperspace? Now, if we have more than 3 dimensions, then we call that space as hyperspace. How do we do it? First we Stack Overflow data dump that contain software specific texts. Our corpus contains about 2.1 million questions and answers. We do pre-processing and feed the contents to FastText. Now FastText generates a three-layer neural network model. This model essentially represents the whole vocabulary like this in a hyperspace. Now how does it help?
• #19: Here we see that burger is close sandwich. Why? They are eaten together. I do that all the time. Well, that is not the case. They are mentioned in the similar contexts by the people across the whole corpus. The model recognizes such occurrences and thus put burger and sandwich close together. Similarly, dumpling and ramen are close to each other. Now, we propose this. This is original query, and this is reformulated query. Now, a good reformulated query will cluster together the original query. A bad reformulated query will NOT be able to cluster with the original query. So, clustering tendency within the hyperspace is our weapon here. We calculated Hopkins statistic and Polygon Area for calculating the clustering tendency.
• #21: Since we used query reformulation in the context of bug localization We performed evaluation in two different dimensions: -- bug localization -- query reformulation. Our approach contributes in both dimensions. We use four standard metrics such as Hit@K, mean avearge precision, mean reciprocal rank and Query Effectiveness. We answer 7 research questions in our work.
• #22: Now, I am not going to discuss those studies in details. But here is the glimpse. Developers generally look for relevant code on the web using natural language query. Please note that we are not talking about simply web search, rather talking about source code repository such as GitHub. Now, GitHub provides this result. Now, you see it tries to match the query keywords with comment and identifiers. But what we are dealing with source code right? So, we need source code friendly query for a better result. So, we identify relevant API classes against this natural language query through extensive data mining and data analytics. And once again, Stack Overflow is our friend in this grand challenge.
• #24: OK! Now we are done with the literature survey. Now, we will focus on the third part, the future research opportunities.
• #26: Here we see that burger is close sandwich. Why? They are eaten together. I do that all the time. Well, that is not the case. They are mentioned in the similar contexts by the people across the whole corpus. The model recognizes such occurrences and thus put burger and sandwich close together. Similarly, dumpling and ramen are close to each other. Now, we propose this. This is original query, and this is reformulated query. Now, a good reformulated query will cluster together the original query. A bad reformulated query will NOT be able to cluster with the original query. So, clustering tendency within the hyperspace is our weapon here. We calculated Hopkins statistic and Polygon Area for calculating the clustering tendency.
• #27: Now lets expand and generalize the problem a bit. So far, we discuss the code search within a local codebase. It could also be in the large-scale open source repository such as GitHub. Now, based on these contexts, there are different challenges in query reformulation. The local codebase is small, domain specific and organized. On the contrary, GitHub is huge, cross-domain and very noisy. So, yes, they need different strategies to suggest queries for them.
• #28: Now once such items are extracted, we split them. Now as we see, these single terms share some kind of semantics to convey a broader semantic. That is, they complement each other in this context. Now, we capture such semantic dependencies in the source code, and develop a term graph like this.
• #30: That is, each of three people, customer, past developer and JOE have their own vocabulary to describe a certain problem/concept. In fact, any people will discuss the same problem with the same vocabulary, this probability is only 15%-20% So, naturally, developer JOE finds it a great challenge to make a connection between bug report and the buggy code. This costs development time, money and valuable efforts.
• #31: Here we see that burger is close sandwich. Why? They are eaten together. I do that all the time. Well, that is not the case. They are mentioned in the similar contexts by the people across the whole corpus. The model recognizes such occurrences and thus put burger and sandwich close together. Similarly, dumpling and ramen are close to each other. Now, we propose this. This is original query, and this is reformulated query. Now, a good reformulated query will cluster together the original query. A bad reformulated query will NOT be able to cluster with the original query. So, clustering tendency within the hyperspace is our weapon here. We calculated Hopkins statistic and Polygon Area for calculating the clustering tendency.
• #33: So, from a noisy report, we extract The report title The encountered exception The most important keywords from the stack traces. Then we do the search with this newly constructed query. For example, the baseline noisy query returns the result at 53rd position. Whereas our query returns the correct result at the topmost position.
• #35: Now the question is, why is this so challenging? The answer is vocabulary mismatch problem. In fact, this is a common problem for any type of document search. Here we see both guys are looking at the same object, but they are explaining it differently. That is, they are both correct from their perspective, but wrong from other guy’s perspective. This also actually happens with bug reports as well. Both customer and developer will explain the same problem using the same terminologies, that probability is only 15% That is why selecting appropriate keywords from the bug report is very challenging.
• #36: Let us see an example. This is a bug report, this is title and this is the description. Now, developer JOE would use this bug report to localize the bug from source code. Now he chose some ad hoc queries. Which one is the best do you think, here? PAUSE! Well, lets see. This one returns the correct result at this position. That means, the developer needs to check 1300+ results b4 reaching to the correct result he tries this query. … oh… this one is the best. So, selecting appropriate keywords from the bug report is not that simple.